Systems and Methods for Real-Time Forecasting and Predicting of Electrical Peaks and Managing the Energy, Health, Reliability, and Performance of Electrical Power Systems Based on an Artificial Adaptive Neural Network

ABSTRACT

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §119(e) of U.S.Provisional Application Ser. No. 60/986,139 filed Nov. 7, 2007. Thisapplication also claims priority as a Continuation of U.S. patentapplication Ser. No. 14/575,446, filed Dec. 18, 2014, which claimspriority as a Continuation of U.S. patent application Ser. No.12/267,346, filed Nov. 7, 2008, and a Continuation-In-Part under 35U.S.C. §120 to U.S. patent application Ser. No. 11/734,706, filed Apr.12, 2007 and entitled “Systems and Methods for Predictive MonitoringIncluding Real-Time Strength and Security Analysis in an ElectricalPower Distribution System,” which in turn claims priority as aContinuation-In-Part under 35 U.S.C. §120 to U.S. patent applicationSer. No. 11/717,378, filed Mar. 12, 2007 and entitled “Systems andMethods for Real-Time Protective Device Evaluation in an ElectricalPower Distribution System.” and to U.S. Provisional Patent ApplicationSer. No. 60/792,175 filed Apr. 12, 2006. The disclosures of theabove-identified applications are incorporated herein by reference as ifset forth in full.

BACKGROUND OF THE INVENTION

I. Field of Use

The present invention relates generally to computer modeling andmanagement of systems and, more particularly, to computer simulationtechniques with real-time system monitoring and prediction of electricalsystem performance.

II. Background

Computer models of complex systems enable improved system design,development, and implementation through techniques for off-linesimulation of the system operation. That is, system models can becreated that computers can “operate” in a virtual environment todetermine design parameters. All manner of systems can be modeled,designed, and operated in this way, including machinery, factories,electrical power and distribution systems, processing plants, devices,chemical processes, biological systems, and the like. Such simulationtechniques have resulted in reduced development costs and superioroperation.

Design and production processes have benefited greatly from suchcomputer simulation techniques, and such techniques are relatively welldeveloped, but such techniques have not been applied in real-time, e.g.,for real-time operational monitoring and management. In addition,predictive failure analysis techniques do not generally use real-timedata that reflect actual system operation. Greater efforts at real-timeoperational monitoring and management would provide more accurate andtimely suggestions for operational decisions, and such techniquesapplied to failure analysis would provide improved predictions of systemproblems before they occur. With such improved techniques, operationalcosts could be greatly reduced.

For example, mission critical electrical systems, e.g., for data centersor nuclear power facilities, must be designed to ensure that power isalways available. Thus, the systems must be as failure proof aspossible, and many layers of redundancy must be designed in to ensurethat there is always a backup in case of a failure. It will beunderstood that such systems are highly complex, a complexity made evengreater as a result of the required redundancy. Computer design andmodeling programs allow for the design of such systems by allowing adesigner to model the system and simulate its operation. Thus, thedesigner can ensure that the system will operate as intended before thefacility is constructed.

Once the facility is constructed, however, the design is typically onlyreferred to when there is a failure. In other words, once there isfailure, the system design is used to trace the failure and takecorrective action; however, because such design are so complex, andthere are many interdependencies, it can be extremely difficult and timeconsuming to track the failure and all its dependencies and then takecorrective action that doesn't result in other system disturbances.

Moreover, changing or upgrading the system can similarly be timeconsuming and expensive, requiring an expert to model the potentialchange, e.g., using the design and modeling program. Unfortunately,system interdependencies can be difficult to simulate, making even minorchanges risky.

For example, no reliable means exists for predicting in real-time thewithstand capabilities, or bracing of protective devices, e.g., lowvoltage, medium voltage and high voltage circuit breakers, fuses, andswitches, and the health of an electrical power system that takes intoconsideration a virtual model that “ages” with the actual facility.Conventional systems use a rigid simulation model that does not take theactual power system alignment and aging effects into consideration whencomputing predicted electrical values.

A model that can align itself in real-time with the actual power systemconfiguration and ages with a facility is critical in obtainingpredictions that are reflective of, e.g., a protective device's abilityto withstand faults and the power system's health and performance inrelation to the life cycle of the system, the operational reliabilityand stability of the system when subjected to contingency conditions,the various operational parameters associated with an alternatingcurrent (AC) arc flash incident, etc. Likewise, real-time data feed(s)from sensor(s) placed throughout the power facility can be supplied to aneural network based processing engine that can utilize the patterns“learned” from the data to make inferences (i.e., predictions) that aremore accurate and reflective of the actual operational performance ofthe power system.

Without real-time synchronization between the virtual system model andthe actual power facility and a modeling engine that can “learn” fromreal-time data feed(s), predictions become of little value as they arenot reflective of the actual power system facility's operational statusand may lead to false conclusions.

SUMMARY

Systems and methods for utilizing a neural network to make real-timepredictions about the health, reliability, and performance of amonitored system are disclosed.

In one aspect, a system for utilizing a neural network algorithmutilized to make real-time predictions about the health, reliability,and performance of a monitored system is disclosed. The system includesa data acquisition component, a power analytics server, and a clientterminal. The data acquisition component is communicatively connected toa sensor configured to acquire real-time data output from the electricalsystem. The power analytics server is communicatively connected to thedata acquisition component and is comprised of a virtual system modelingengine, an analytics engine, an adaptive prediction engine.

The virtual system modeling engine is configured to generate predicteddata output for the electrical system utilizing a virtual system modelof the electrical system. The analytics engine is configured to monitorthe real-time data output and the predicted data output of theelectrical system initiating a calibration and synchronization operationto update the virtual system model when a difference between thereal-time data output and the predicted data output exceeds a threshold.The adaptive prediction engine can be configured to forecast an aspectof the monitored system using a neural network algorithm. The adaptiveprediction engine is further configured to process the real-time dataoutput and automatically optimize the neural network algorithm byminimizing a measure of error between the real-time data output and anestimated data output predicted by the neural network algorithm.

The client terminal is communicatively connected to the power analyticsserver and configured to display the forecasted aspect.

In another aspect, a method for utilizing a neural network algorithmutilized to make real-time predictions about the health, reliability,and performance of a monitored system is disclosed. Real-time dataoutput is received from one or more sensors interfaced to the monitoredsystem. Predicted data output is generated for the one or more sensorsinterfaced to the monitored system utilizing a virtual system model ofthe monitored system. The virtual system model of the monitored systemis calibrated when a difference between the real-time data output andthe predicted data output exceeds a threshold. The real-time data outputis processed using a neural network algorithm. The neural networkalgorithm is optimized by minimizing a measure of error between thereal-time data output and an estimated data output predicted by theneural network algorithm. An aspect of the monitored system isforecasted using the neural network algorithm.

These and other features, aspects, and embodiments are described belowin the section entitled “Detailed Description.”

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the principles disclosed herein,and the advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is an illustration of a system for utilizing real-time data forpredictive analysis of the performance of a monitored system, inaccordance with one embodiment.

FIG. 2 is a diagram illustrating a detailed view of an analytics serverincluded in the system of FIG. 1, in accordance with one embodiment.

FIG. 3 is a diagram illustrating how the system of FIG. 1 operates tosynchronize the operating parameters between a physical facility and avirtual system model of the facility, in accordance with one embodiment.

FIG. 4 is an illustration of the scalability of a system for utilizingreal-time data for predictive analysis of the performance of a monitoredsystem, in accordance with one embodiment.

FIG. 5 is a block diagram that shows the configuration details of thesystem illustrated in FIG. 1, in accordance with one embodiment.

FIG. 6 is an illustration of a flowchart describing a method forreal-time monitoring and predictive analysis of a monitored system, inaccordance with one embodiment.

FIG. 7 is an illustration of a flowchart describing a method formanaging real-time updates to a virtual system model of a monitoredsystem, in accordance with one embodiment.

FIG. 8 is an illustration of a flowchart describing a method forsynchronizing real-time system data with a virtual system model of amonitored system, in accordance with one embodiment.

FIG. 9 is a flow chart illustrating an example method for updating thevirtual model, in accordance with one embodiment.

FIG. 10 is a diagram illustrating an example process for monitoring thestatus of protective devices in a monitored system and updating avirtual model based on monitored data, in accordance with oneembodiment.

FIG. 11 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored,in accordance with one embodiment.

FIG. 12 is a diagram illustrating an example process for determining theprotective capabilities of a High Voltage Circuit Breaker (HVCB), inaccordance with one embodiment.

FIG. 13 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored,in accordance with another embodiment.

FIG. 14 is a diagram illustrating a process for evaluating the withstandcapabilities of a MVCB, in accordance with one embodiment

FIG. 15 is a flow chart illustrating an example process for analyzingthe reliability of an electrical power distribution and transmissionsystem, in accordance with one embodiment.

FIG. 16 is a flow chart illustrating an example process for analyzingthe reliability of an electrical power distribution and transmissionsystem that takes weather information into account, in accordance withone embodiment.

FIG. 17 is a diagram illustrating an example process for predicting inreal-time various parameters associated with an alternating current (AC)arc flash incident, in accordance with one embodiment.

FIG. 18 is a flow chart illustrating an example process for real-timeanalysis of the operational stability of an electrical powerdistribution and transmission system in accordance with one embodiment.

FIG. 19 is a diagram illustrating how the HTM Pattern Recognition andMachine Learning Engine works in conjunction with the other elements ofthe analytics system to make predictions about the operational aspectsof a monitored system, in accordance with one embodiment.

FIG. 20 is an illustration of the various cognitive layers that comprisethe neocortical catalyst process used by the HTM Pattern Recognition andMachine Learning Engine to analyze and make predictions about theoperational aspects of a monitored system, in accordance with oneembodiment.

FIG. 21 is a logical representation of how a three-layer feed-forwardneural network functions, in accordance with one embodiment.

FIG. 22 is a logical representation of a compact form of the three-layerfeed-forward neural network, in accordance with one embodiment.

FIG. 23 is an illustration of a matrices depicting how a three-layerfeed-forward neural network can be trained using known inputs and outputvalues, in accordance with one embodiment.

FIG. 24 illustrates an example of how training patterns can be used totrain and validate the accuracy of a neural network, in accordance toone embodiment.

FIG. 25 is a table summarizing the SSE values resulting from thevalidation of a neural network using a set of validation patterns, inaccordance with one embodiment.

FIG. 26 is an illustration of a flow chart describing a method forutilizing a neural network algorithm utilized to make real-timepredictions about the health, reliability, and performance of anelectrical system, in accordance with one embodiment.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Systems and methods for utilizing a neural network to make real-timepredictions about the health, reliability, and performance of amonitored system are disclosed. It will be clear, however, that thepresent invention may be practiced without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail in order not to unnecessarily obscure the presentinvention.

As used herein, a system denotes a set of components, real or abstract,comprising a whole where each component interacts with or is related toat least one other component within the whole. Examples of systemsinclude machinery, factories, electrical systems, processing plants,devices, chemical processes, biological systems, data centers, aircraftcarriers, and the like. An electrical system can designate a powergeneration and/or distribution system that is widely dispersed (i.e.,power generation, transformers, and/or electrical distributioncomponents distributed geographically throughout a large region) orbounded within a particular location (e.g., a power plant within aproduction facility, a bounded geographic area, on board a ship, afactory, a data center, etc.).

A network application is any application that is stored on anapplication server connected to a network (e.g., local area network,wide area network, etc.) in accordance with any contemporaryclient/server architecture model and can be accessed via the network. Inthis arrangement, the network application programming interface (API)resides on the application server separate from the client machine. Theclient interface would typically be a web browser (e.g. INTERNETEXPLORER™, FIREFOX™, NETSCAPE™, etc) that is in communication with thenetwork application server via a network connection (e.g., HTTP, HTTPS,RSS, etc.).

FIG. 1 is an illustration of a system for utilizing real-time data forpredictive analysis of the performance of a monitored system, inaccordance with one embodiment. As shown herein, the system 100 includesa series of sensors (i.e., Sensor A 104, Sensor B 106, Sensor C 108)interfaced with the various components of a monitored system 102, a dataacquisition hub 112, an analytics server 116, and a thin-client device128. In one embodiment, the monitored system 102 is an electrical powergeneration plant. In another embodiment, the monitored system 102 is anelectrical power transmission infrastructure. In still anotherembodiment, the monitored system 102 is an electrical power distributionsystem. In still another embodiment, the monitored system 102 includes acombination of one or more electrical power generation plant(s), powertransmission infrastructure(s), and/or an electrical power distributionsystem. It should be understood that the monitored system 102 can be anycombination of components whose operations can be monitored withconventional sensors and where each component interacts with or isrelated to at least one other component within the combination. For amonitored system 102 that is an electrical power generation,transmission, or distribution system, the sensors can provide data suchas voltage, frequency, current, power, power factor, and the like.

The sensors are configured to provide output values for systemparameters that indicate the operational status and/or “health” of themonitored system 102. For example, in an electrical power generationsystem, the current output or voltage readings for the variouscomponents that comprise the power generation system is indicative ofthe overall health and/or operational condition of the system. In oneembodiment, the sensors are configured to also measure additional datathat can affect system operation. For example, for an electrical powerdistribution system, the sensor output can include environmentalinformation, e.g., temperature, humidity, etc., which can impactelectrical power demand and can also affect the operation and efficiencyof the power distribution system itself.

Continuing with FIG. 1, in one embodiment, the sensors are configured tooutput data in an analog format. For example, electrical power sensormeasurements (e.g., voltage, current, etc.) are sometimes conveyed in ananalog format as the measurements may be continuous in both time andamplitude. In another embodiment, the sensors are configured to outputdata in a digital format. For example, the same electrical power sensormeasurements may be taken in discrete time increments that are notcontinuous in time or amplitude. In still another embodiment, thesensors are configured to output data in either an analog or digitalformat depending on the sampling requirements of the monitored system102.

The sensors can be configured to capture output data at split-secondintervals to effectuate “real time” data capture. For example, in oneembodiment, the sensors can be configured to generate hundreds ofthousands of data readings per second. It should be appreciated,however, that the number of data output readings taken by a sensor maybe set to any value as long as the operational limits of the sensor andthe data processing capabilities of the data acquisition hub 112 are notexceeded.

Still with FIG. 1, each sensor is communicatively connected to the dataacquisition hub 112 via an analog or digital data connection 110. Thedata acquisition hub 112 may be a standalone unit or integrated withinthe analytics server 116 and can be embodied as a piece of hardware,software, or some combination thereof. In one embodiment, the dataconnection 110 is a “hard wired” physical data connection (e.g., serial,network, etc.). For example, a serial or parallel cable connectionbetween the sensor and the hub 112. In another embodiment, the dataconnection 110 is a wireless data connection. For example, a radiofrequency (RF), BLUETOOTH™, infrared or equivalent connection betweenthe sensor and the hub 112.

The data acquisition hub 112 is configured to communicate “real-time”data from the monitored system 102 to the analytics server 116 using anetwork connection 114. In one embodiment, the network connection 114 isa “hardwired” physical connection. For example, the data acquisition hub112 may be communicatively connected (via Category 5 (CAT5), fiber opticor equivalent cabling) to a data server (not shown) that iscommunicatively connected (via CAT5, fiber optic or equivalent cabling)through the Internet and to the analytics server 116 server. Theanalytics server 116 being also communicatively connected with theInternet (via CAT5, fiber optic, or equivalent cabling). In anotherembodiment, the network connection 114 is a wireless network connection(e.g., Wi-Fi, WLAN, etc.). For example, utilizing an 802.11b/g orequivalent transmission format. In practice, the network connectionutilized is dependent upon the particular requirements of the monitoredsystem 102.

Data acquisition hub 112 can also be configured to supply warning andalarms signals as well as control signals to monitored system 102 and/orsensors 104, 106, and 108 as described in more detail below.

As shown in FIG. 1, in one embodiment, the analytics server 116 hosts ananalytics engine 118, virtual system modeling engine 124 and severaldatabases 126, 130, and 132. The virtual system modeling engine can,e.g., be a computer modeling system, such as described above. In thiscontext, however, the modeling engine can be used to precisely model andmirror the actual electrical system. Analytics engine 118 can beconfigured to generate predicted data for the monitored system andanalyze difference between the predicted data and the real-time datareceived from hub 112.

FIG. 2 is a diagram illustrating a more detailed view of analytic server116. As can be seen, analytic server 116 is interfaced with a monitoredfacility 102 via sensors 202, e.g., sensors 104, 106, and 108. Sensors202 are configured to supply real-time data from within monitoredfacility 102. The real-time data is communicated to analytic server 116via a hub 204. Hub 204 can be configure to provide real-time data toserver 116 as well as alarming, sensing and control featured forfacility 102.

The real-time data from hub 204 can be passed to a comparison engine210, which can form part of analytics engine 118. Comparison engine 210can be configured to continuously compare the real-time data withpredicted values generated by simulation engine 208. Based on thecomparison, comparison engine 210 can be further configured to determinewhether deviations between the real-time and the expected values exists,and if so to classify the deviation, e.g., high, marginal, low, etc. Thedeviation level can then be communicated to decision engine 212, whichcan also comprise part of analytics engine 118.

Decision engine 212 can be configured to look for significant deviationsbetween the predicted values and real-time values as received from thecomparison engine 210. If significant deviations are detected, decisionengine 212 can also be configured to determine whether an alarmcondition exists, activate the alarm and communicate the alarm toHuman-Machine Interface (HMI) 214 for display in real-time via, e.g.,thin client 128. Decision engine 212 can also be configured to performroot cause analysis for significant deviations in order to determine theinterdependencies and identify the parent-child failure relationshipsthat may be occurring. In this manner, parent alarm conditions are notdrowned out by multiple children alarm conditions, allowing theuser/operator to focus on the main problem, at least at first.

Thus, in one embodiment, and alarm condition for the parent can bedisplayed via HMI 214 along with an indication that processes andequipment dependent on the parent process or equipment are also in alarmcondition. This also means that server 116 can maintain a parent-childlogical relationship between processes and equipment comprising facility102. Further, the processes can be classified as critical, essential,non-essential, etc.

Decision engine 212 can also be configured to determine health andperformance levels and indicate these levels for the various processesand equipment via HMI 214. All of which, when combined with the analyticcapabilities of analytics engine 118 allows the operator to minimize therisk of catastrophic equipment failure by predicting future failures andproviding prompt, informative information concerning potential/predictedfailures before they occur. Avoiding catastrophic failures reduces riskand cost, and maximizes facility performance and up time.

Simulation engine 208 operates on complex logical models 206 of facility102. These models are continuously and automatically synchronized withthe actual facility status based on the real-time data provided by hub204. In other words, the models are updated based on current switchstatus, breaker status, e.g., open-closed, equipment on/off status, etc.Thus, the models are automatically updated based on such status, whichallows simulation engine to produce predicted data based on the currentfacility status. This in turn, allows accurate and meaningfulcomparisons of the real-time data to the predicted data.

Example models 206 that can be maintained and used by server 116 includepower flow models used to calculate expected kW, kVAR, power factorvalues, etc., short circuit models used to calculate maximum and minimumavailable fault currents, protection models used to determine properprotection schemes and ensure selective coordination of protectivedevices, power quality models used to determine voltage and currentdistortions at any point in the network, to name just a few. It will beunderstood that different models can be used depending on the systembeing modeled.

In certain embodiments, hub 204 is configured to supply equipmentidentification associated with the real-time data. This identificationcan be cross referenced with identifications provided in the models.

In one embodiment, if the comparison performed by comparison engine 210indicates that the differential between the real-time sensor outputvalue and the expected value exceeds a Defined Difference Tolerance(DDT) value (i.e., the “real-time” output values of the sensor output donot indicate an alarm condition) but below an alarm condition (i.e.,alarm threshold value), a calibration request is generated by theanalytics engine 118. If the differential exceeds, the alarm condition,an alarm or notification message is generated by the analytics engine118. If the differential is below the DTT value, the analytics enginedoes nothing and continues to monitor the real-time data and expecteddata.

In one embodiment, the alarm or notification message is sent directly tothe client (i.e., user) 128, e.g., via HMI 214, for display in real-timeon a web browser, pop-up message box, e-mail, or equivalent on theclient 128 display panel. In another embodiment, the alarm ornotification message is sent to a wireless mobile device (e.g.,BLACKBERRY™, laptop, pager, etc.) to be displayed for the user by way ofa wireless router or equivalent device interfaced with the analyticsserver 116. In still another embodiment, the alarm or notificationmessage is sent to both the client 128 display and the wireless mobiledevice. The alarm can be indicative of a need for a repair event ormaintenance to be done on the monitored system. It should be noted,however, that calibration requests should not be allowed if an alarmcondition exists to prevent the models form being calibrated to anabnormal state.

Once the calibration is generated by the analytics engine 118, thevarious operating parameters or conditions of model(s) 206 can beupdated or adjusted to reflect the actual facility configuration. Thiscan include, but is not limited to, modifying the predicted data outputfrom the simulation engine 208, adjusting the logic/processingparameters utilized by the model(s) 206, adding/subtracting functionalelements from model(s) 206, etc. It should be understood, that anyoperational parameter of models 206 can be modified as long as theresulting modifications can be processed and registered by simulationengine 208.

Referring back to FIG. 1, models 206 can be stored in the virtual systemmodel database 126. As noted, a variety of conventional virtual modelapplications can be used for creating a virtual system model, so that awide variety of systems and system parameters can be modeled. Forexample, in the context of an electrical power distribution system, thevirtual system model can include components for modeling reliability,voltage stability, and power flow. In addition, models 206 can includedynamic control logic that permits a user to configure the models 206 byspecifying control algorithms and logic blocks in addition tocombinations and interconnections of generators, governors, relays,breakers, transmission line, and the like. The voltage stabilityparameters can indicate capacity in terms of size, supply, anddistribution, and can indicate availability in terms of remainingcapacity of the presently configured system. The power flow model canspecify voltage, frequency, and power factor, thus representing the“health” of the system.

All of models 206 can be referred to as a virtual system model. Thus,virtual system model database can be configured to store the virtualsystem model. A duplicate, but synchronized copy of the virtual systemmodel can be stored in a virtual simulation model database 130. Thisduplicate model can be used for what-if simulations. In other words,this model can be used to allow a system designer to make hypotheticalchanges to the facility and test the resulting effect, without takingdown the facility or costly and time consuming analysis. Suchhypothetical can be used to learn failure patterns and signatures aswell as to test proposed modifications, upgrades, additions, etc., forthe facility. The real-time data, as well as trending produced byanalytics engine 118 can be stored in a real-time data acquisitiondatabase 132.

As discussed above, the virtual system model is periodically calibratedand synchronized with “real-time” sensor data outputs so that thevirtual system model provides data output values that are consistentwith the actual “real-time” values received from the sensor outputsignals. Unlike conventional systems that use virtual system modelsprimarily for system design and implementation purposes (i.e., offlinesimulation and facility planning), the virtual system models describedherein are updated and calibrated with the real-time system operationaldata to provide better predictive output values. A divergence betweenthe real-time sensor output values and the predicted output valuesgenerate either an alarm condition for the values in question and/or acalibration request that is sent to the calibration engine 134.

Continuing with FIG. 1, the analytics engine 118 can be configured toimplement pattern/sequence recognition into a real-time decision loopthat, e.g., is enabled by a new type of machine learning calledassociative memory, or hierarchical temporal memory (HTM), which is abiological approach to learning and pattern recognition. Associativememory allows storage, discovery, and retrieval of learned associationsbetween extremely large numbers of attributes in real time. At a basiclevel, an associative memory stores information about how attributes andtheir respective features occur together. The predictive power of theassociative memory technology comes from its ability to interpret andanalyze these co-occurrences and to produce various metrics. Associativememory is built through “experiential” learning in which each newlyobserved state is accumulated in the associative memory as a basis forinterpreting future events. Thus, by observing normal system operationover time, and the normal predicted system operation over time, theassociative memory is able to learn normal patterns as a basis foridentifying non-normal behavior and appropriate responses, and toassociate patterns with particular outcomes, contexts or responses. Theanalytics engine 118 is also better able to understand component meantime to failure rates through observation and system availabilitycharacteristics. This technology in combination with the virtual systemmodel can be characterized as a “neocortical” model of the system undermanagement.

This approach also presents a novel way to digest and comprehend alarmsin a manageable and coherent way. The neocortical model could assist inuncovering the patterns and sequencing of alarms to help pinpoint thelocation of the (impending) failure, its context, and even the cause.Typically, responding to the alarms is done manually by experts who havegained familiarity with the system through years of experience. However,at times, the amount of information is so great that an individualcannot respond fast enough or does not have the necessary expertise. An“intelligent” system like the neocortical system that observes andrecommends possible responses could improve the alarm management processby either supporting the existing operator, or even managing the systemautonomously.

Current simulation approaches for maintaining transient stabilityinvolve traditional numerical techniques and typically do not test allpossible scenarios. The problem is further complicated as the numbers ofcomponents and pathways increase. Through the application of theneocortical model, by observing simulations of circuits, and bycomparing them to actual system responses, it may be possible to improvethe simulation process, thereby improving the overall design of futurecircuits.

The virtual system model database 126, as well as databases 130 and 132,can be configured to store one or more virtual system models, virtualsimulation models, and real-time data values, each customized to aparticular system being monitored by the analytics server 118. Thus, theanalytics server 118 can be utilized to monitor more than one system ata time. As depicted herein, the databases 126, 130, and 132 can behosted on the analytics server 116 and communicatively interfaced withthe analytics engine 118. In other embodiments, databases 126, 130, and132 can be hosted on a separate database server (not shown) that iscommunicatively connected to the analytics server 116 in a manner thatallows the virtual system modeling engine 124 and analytics engine 118to access the databases as needed.

Therefore, in one embodiment, the client 128 can modify the virtualsystem model stored on the virtual system model database 126 by using avirtual system model development interface using well-known modelingtools that are separate from the other network interfaces. For example,dedicated software applications that run in conjunction with the networkinterface to allow a client 128 to create or modify the virtual systemmodels.

The client 128 may utilize a variety of network interfaces (e.g., webbrowser, CITRIX™, WINDOWS TERMINAL SERVICES™, telnet, or otherequivalent thin-client terminal applications, etc.) to access,configure, and modify the sensors (e.g., configuration files, etc.),analytics engine 118 (e.g., configuration files, analytics logic, etc.),calibration parameters (e.g., configuration files, calibrationparameters, etc.), virtual system modeling engine 124 (e.g.,configuration files, simulation parameters, etc.) and virtual systemmodel of the system under management (e.g., virtual system modeloperating parameters and configuration files). Correspondingly, datafrom those various components of the monitored system 102 can bedisplayed on a client 128 display panel for viewing by a systemadministrator or equivalent.

As described above, server 116 is configured to synchronize the physicalworld with the virtual and report, e.g., via visual, real-time display,deviations between the two as well as system health, alarm conditions,predicted failures, etc. This is illustrated with the aid of FIG. 3, inwhich the synchronization of the physical world (left side) and virtualworld (right side) is illustrated. In the physical world, sensors 202produce real-time data 302 for the processes 312 and equipment 314 thatmake up facility 102. In the virtual world, simulations 304 of thevirtual system model 206 provide predicted values 306, which arecorrelated and synchronized with the real-time data 302. The real-timedata can then be compared to the predicted values so that differences308 can be detected. The significance of these differences can bedetermined to determine the health status 310 of the system. The healthstats can then be communicated to the processes 312 and equipment 314,e.g., via alarms and indicators, as well as to thin client 128, e.g.,via web pages 316.

FIG. 4 is an illustration of the scalability of a system for utilizingreal-time data for predictive analysis of the performance of a monitoredsystem, in accordance with one embodiment. As depicted herein, ananalytics central server 422 is communicatively connected with analyticsserver A 414, analytics server B 416, and analytics server n 418 (i.e.,one or more other analytics servers) by way of one or more networkconnections 114. Each of the analytics servers is communicativelyconnected with a respective data acquisition hub (i.e., Hub A 408, Hub B410, Hub n 412) that communicates with one or more sensors that areinterfaced with a system (i.e., Monitored System A 402, Monitored SystemB 404, Monitored System n 406) that the respective analytical servermonitors. For example, analytics server A414 is communicative connectedwith data acquisition hub A 408, which communicates with one or moresensors interfaced with monitored system A 402.

Each analytics server (i.e., analytics server A 414, analytics server B416, analytics server n 418) is configured to monitor the sensor outputdata of its corresponding monitored system and feed that data to thecentral analytics server 422. Additionally, each of the analyticsservers can function as a proxy agent of the central analytics server422 during the modifying and/or adjusting of the operating parameters ofthe system sensors they monitor. For example, analytics server B 416 isconfigured to be utilized as a proxy to modify the operating parametersof the sensors interfaced with monitored system B 404.

Moreover, the central analytics server 422, which is communicativelyconnected to one or more analytics server(s) can be used to enhance thescalability. For example, a central analytics server 422 can be used tomonitor multiple electrical power generation facilities (i.e., monitoredsystem A 402 can be a power generation facility located in city A whilemonitored system B 404 is a power generation facility located in city B)on an electrical power grid. In this example, the number of electricalpower generation facilities that can be monitored by central analyticsserver 422 is limited only by the data processing capacity of thecentral analytics server 422. The central analytics server 422 can beconfigured to enable a client 128 to modify and adjust the operationalparameters of any the analytics servers communicatively connected to thecentral analytics server 422. Furthermore, as discussed above, each ofthe analytics servers are configured to serve as proxies for the centralanalytics server 422 to enable a client 128 to modify and/or adjust theoperating parameters of the sensors interfaced with the systems thatthey respectively monitor. For example, the client 128 can use thecentral analytics server 422, and vice versa, to modify and/or adjustthe operating parameters of analytics server A 414 and utilize the sameto modify and/or adjust the operating parameters of the sensorsinterfaced with monitored system A 402. Additionally, each of theanalytics servers can be configured to allow a client 128 to modify thevirtual system model through a virtual system model developmentinterface using well-known modeling tools.

In one embodiment, the central analytics server 422 can function tomonitor and control a monitored system when its corresponding analyticsserver is out of operation. For example, central analytics server 422can take over the functionality of analytics server B 416 when theserver 416 is out of operation. That is, the central analytics server422 can monitor the data output from monitored system B 404 and modifyand/or adjust the operating parameters of the sensors that areinterfaced with the system 404.

In one embodiment, the network connection 114 is established through awide area network (WAN) such as the Internet. In another embodiment, thenetwork connection is established through a local area network (LAN)such as the company intranet. In a separate embodiment, the networkconnection 114 is a “hardwired” physical connection. For example, thedata acquisition hub 112 may be communicatively connected (via Category5 (CAT5), fiber optic or equivalent cabling) to a data server that iscommunicatively connected (via CAT5, fiber optic or equivalent cabling)through the Internet and to the analytics server 116 server hosting theanalytics engine 118. In another embodiment, the network connection 114is a wireless network connection (e.g., Wi-Fi, WLAN, etc.). For example,utilizing an 802.11b/g or equivalent transmission format.

In certain embodiments, regional analytics servers can be placed betweenlocal analytics servers 414, 416, . . . , 418 and central analyticsserver 422. Further, in certain embodiments a disaster recovery site canbe included at the central analytics server 422 level.

FIG. 5 is a block diagram that shows the configuration details ofanalytics server 116 illustrated in FIG. 1 in more detail. It should beunderstood that the configuration details in FIG. 5 are merely oneembodiment of the items described for FIG. 1, and it should beunderstood that alternate configurations and arrangements of componentscould also provide the functionality described herein.

The analytics server 116 includes a variety of components. In the FIG. 5embodiment, the analytics server 116 is implemented in a Web-basedconfiguration, so that the analytics server 116 includes (orcommunicates with) a secure web server 530 for communication with thesensor systems 519 (e.g., data acquisition units, metering devices,sensors, etc.) and external communication entities 534 (e.g., webbrowser, “thin client” applications, etc.). A variety of user views andfunctions 532 are available to the client 128 such as: alarm reports.Active X controls, equipment views, view editor tool, custom userinterface page, and XML parser. It should be appreciated, however, thatthese are just examples of a few in a long list of views and functions532 that the analytics server 116 can deliver to the externalcommunications entities 534 and are not meant to limit the types ofviews and functions 532 available to the analytics server 116 in anyway.

The analytics server 116 also includes an alarm engine 506 and messagingengine 504, for the aforementioned external communications. The alarmengine 506 is configured to work in conjunction with the messagingengine 504 to generate alarm or notification messages 502 (in the formof text messages, c-mails, paging, etc.) in response to the alarmconditions previously described. The analytics server 116 determinesalarm conditions based on output data it receives from the varioussensor systems 519 through a communications connection (e.g., wireless516, TCP/IP 518, Serial 520, etc.) and simulated output data from avirtual system model 512, of the monitored system, processed by theanalytics engines 118. In one embodiment, the virtual system model 512is created by a user through interacting with an external communicationentity 534 by specifying the components that comprise the monitoredsystem and by specifying relationships between the components of themonitored system. In another embodiment, the virtual system model 512 isautomatically generated by the analytics engines 118 as components ofthe monitored system are brought online and interfaced with theanalytics server 508.

Continuing with FIG. 5, a virtual system model database 526 iscommunicatively connected with the analytics server 116 and isconfigured to store one or more virtual system models 512, each of whichrepresents a particular monitored system. For example, the analyticsserver 116 can conceivably monitor multiple electrical power generationsystems (e.g., system A, system B, system C, etc.) spread across a widegeographic area (e.g., City A, City B, City C, etc.). Therefore, theanalytics server 116 will utilize a different virtual system model 512for each of the electrical power generation systems that it monitors.Virtual simulation model database 538 can be configured to store asynchronized, duplicate copy of the virtual system model 512, andreal-time data acquisition database 540 can store the real-time andtrending data for the system(s) being monitored.

Thus, in operation, analytics server 116 can receive real-time data forvarious sensors, i.e., components, through data acquisition system 202.As can be seen, analytics server 116 can comprise various driversconfigured to interface with the various types of sensors, etc.,comprising data acquisition system 202. This data represents thereal-time operational data for the various components. For example, thedata may indicate that a certain component is operating at a certainvoltage level and drawing certain amount of current. This informationcan then be fed to a modeling engine to generate a virtual system model612 that is based on the actual real-time operational data.

Analytics engine 118 can be configured to compare predicted data basedon the virtual system model 512 with real-time data received from dataacquisition system 202 and to identify any differences. In someinstances, analytics engine can be configured to identify thesedifferences and then update, i.e., calibrate, the virtual system model512 for use in future comparisons. In this manner, more accuratecomparisons and warnings can be generated.

But in other instances, the differences will indicate a failure, or thepotential for a failure. For example, when a component begins to fail,the operating parameters will begin to change. This change may be suddenor it may be a progressive change over time. Analytics engine 118 candetect such changes and issue warnings that can allow the changes to bedetected before a failure occurs. The analytic engine 118 can beconfigured to generate warnings that can be communicated via interface532.

For example, a user can access information from server 116 using thinclient 534. For example, reports can be generate and served to thinclient 534 via server 540. These reports can, for example, compriseschematic or symbolic illustrations of the system being monitored.Status information for each component can be illustrated or communicatedfor each component. This information can be numerical, i.e., the voltageor current level. Or it can be symbolic, i.e., green for normal, red forfailure or warning. In certain embodiments, intermediate levels offailure can also be communicated, i.e., yellow can be used to indicateoperational conditions that project the potential for future failure. Itshould be noted that this information can be accessed in real-time.Moreover, via thin client 534, the information can be accessed fromanywhere and anytime.

Continuing with FIG. 5, the Analytics Engine 118 is communicativelyinterfaced with a HTM Pattern Recognition and Machine Learning Engine551. The HTM Engine 551 is configured to work in conjunction with theAnalytics Engine 118 and a virtual system model of the monitored systemto make real-time predictions (i.e., forecasts) about variousoperational aspects of the monitored system. The HTM Engine 551 works byprocessing and storing patterns observed during the normal operation ofthe monitored system over time. These observations are provided in theform of real-time data captured using a multitude of sensors that areimbedded within the monitored system. In one embodiment, the virtualsystem model is also updated with the real-time data such that thevirtual system model “ages” along with the monitored system. Examples ofa monitored system includes machinery, factories, electrical systems,processing plants, devices, chemical processes, biological systems, datacenters, aircraft carriers, and the like. It should be understood thatthe monitored system can be any combination of components whoseoperations can be monitored with conventional sensors and where eachcomponent interacts with or is related to at least one other componentwithin the combination.

FIG. 6 is an illustration of a flowchart describing a method forreal-time monitoring and predictive analysis of a monitored system, inaccordance with one embodiment. Method 600 begins with operation 602where real-time data indicative of the monitored system status isprocessed to enable a virtual model of the monitored system undermanagement to be calibrated and synchronized with the real-time data. Inone embodiment, the monitored system 102 is a mission criticalelectrical power system. In another embodiment, the monitored system 102can include an electrical power transmission infrastructure. In stillanother embodiment, the monitored system 102 includes a combination ofthereof. It should be understood that the monitored system 102 can beany combination of components whose operations can be monitored withconventional sensors and where each component interacts with or isrelated to at least one other component within the combination.

Method 600 moves on to operation 604 where the virtual system model ofthe monitored system under management is updated in response to thereal-time data. This may include, but is not limited to, modifying thesimulated data output from the virtual system model, adjusting thelogic/processing parameters utilized by the virtual system modelingengine to simulate the operation of the monitored system,adding/subtracting functional elements of the virtual system model, etc.It should be understood, that any operational parameter of the virtualsystem modeling engine and/or the virtual system model may be modifiedby the calibration engine as long as the resulting modifications can beprocessed and registered by the virtual system modeling engine.

Method 600 proceeds on to operation 606 where the simulated real-timedata indicative of the monitored system status is compared with acorresponding virtual system model created at the design stage. Thedesign stage models, which may be calibrated and updated based onreal-time monitored data, are used as a basis for the predictedperformance of the system. The real-time monitored data can then providethe actual performance over time. By comparing the real-time time datawith the predicted performance information, difference can be identifieda tracked by, e.g., the analytics engine 118. Analytics engines 118 canthen track trends, determine alarm states, etc., and generate areal-time report of the system status in response to the comparison.

In other words, the analytics can be used to analyze the comparison andreal-time data and determine if there is a problem that should bereported and what level the problem may be, e.g., low priority, highpriority, critical, etc. The analytics can also be used to predictfuture failures and time to failure, etc. In one embodiment, reports canbe displayed on a conventional web browser (e.g. INTERNET EXPLORER™,FIREFOX™, NETSCAPE™, etc) that is rendered on a standard personalcomputing (PC) device. In another embodiment, the “real-time” report canbe rendered on a “thin-client” computing device (e.g., CITRIX™, WINDOWSTERMINAL SERVICES™, telnet, or other equivalent thin-client terminalapplication). In still another embodiment, the report can be displayedon a wireless mobile device (e.g., BLACKBERRY™, laptop, pager, etc.).For example, in one embodiment, the “real-time” report can include suchinformation as the differential in a particular power parameter (i.e.,current, voltage, etc.) between the real-time measurements and thevirtual output data.

FIG. 7 is an illustration of a flowchart describing a method formanaging real-*time updates to a virtual system model of a monitoredsystem, in accordance with one embodiment. Method 700 begins withoperation 702 where real-time data output from a sensor interfaced withthe monitored system is received. The sensor is configured to captureoutput data at split-second intervals to effectuate “real time” datacapture. For example, in one embodiment, the sensor is configured togenerate hundreds of thousands of data readings per second. It should beappreciated, however, that the number of data output readings taken bythe sensor may be set to any value as long as the operational limits ofthe sensor and the data processing capabilities of the data acquisitionhub are not exceeded.

Method 700 moves to operation 704 where the real-time data is processedinto a defined format. This would be a format that can be utilized bythe analytics server to analyze or compare the data with the simulateddata output from the virtual system model. In one embodiment, the datais converted from an analog signal to a digital signal. In anotherembodiment, the data is converted from a digital signal to an analogsignal. It should be understood, however, that the real-time data may beprocessed into any defined format as long as the analytics engine canutilize the resulting data in a comparison with simulated output datafrom a virtual system model of the monitored system.

Method 700 continues on to operation 706 where the predicted (i.e.,simulated) data for the monitored system is generated using a virtualsystem model of the monitored system. As discussed above, a virtualsystem modeling engine utilizes dynamic control logic stored in thevirtual system model to generate the predicted output data. Thepredicted data is supposed to be representative of data that shouldactually be generated and output from the monitored system.

Method 700 proceeds to operation 708 where a determination is made as towhether the difference between the real-time data output and thepredicted system data falls between a set value and an alarm conditionvalue, where if the difference falls between the set value and the alarmcondition value a virtual system model calibration and a response can begenerated. That is, if the comparison indicates that the differentialbetween the “real-time” sensor output value and the corresponding“virtual” model data output value exceeds a Defined Difference Tolerance(DDT) value (i.e., the “real-time” output values of the sensor output donot indicate an alarm condition) but below an alarm condition (i.e.,alarm threshold value), a response can be generated by the analyticsengine. In one embodiment, if the differential exceeds, the alarmcondition, an alarm or notification message is generated by theanalytics engine 118. In another embodiment, if the differential isbelow the DTT value, the analytics engine does nothing and continues tomonitor the “real-time” data and “virtual” data. Generally speaking, thecomparison of the set value and alarm condition is indicative of thefunctionality of one or more components of the monitored system.

FIG. 8 is an illustration of a flowchart describing a method forsynchronizing real-time system data with a virtual system model of amonitored system, in accordance with one embodiment. Method 800 beginswith operation 802 where a virtual system model calibration request isreceived. A virtual model calibration request can be generated by ananalytics engine whenever the difference between the real-time dataoutput and the predicted system data falls between a set value and analarm condition value.

Method 800 proceeds to operation 804 where the predicted system outputvalue for the virtual system model is updated with a real-time outputvalue for the monitored system. For example, if sensors interfaced withthe monitored system outputs a real-time current value of A, then thepredicted system output value for the virtual system model is adjustedto reflect a predicted current value of A.

Method 800 moves on to operation 806 where a difference between thereal-time sensor value measurement from a sensor integrated with themonitored system and a predicted sensor value for the sensor isdetermined. As discussed above, the analytics engine is configured toreceive “real-time” data from sensors interfaced with the monitoredsystem via the data acquisition hub (or, alternatively directly from thesensors) and “virtual” data from the virtual system modeling enginesimulating the data output from a virtual system model of the monitoredsystem. In one embodiment, the values are in units of electrical poweroutput (i.e., current or voltage) from an electrical power generation ortransmission system. It should be appreciated, however, that the valuescan essentially be any unit type as long as the sensors can beconfigured to output data in those units or the analytics engine canconvert the output data received from the sensors into the desired unittype before performing the comparison.

Method 800 continues on to operation 808 where the operating parametersof the virtual system model are adjusted to minimize the difference.This means that the logic parameters of the virtual system model that avirtual system modeling engine uses to simulate the data output fromactual sensors interfaced with the monitored system are adjusted so thatthe difference between the real-time data output and the simulated dataoutput is minimized. Correspondingly, this operation will update andadjust any virtual system model output parameters that are functions ofthe virtual system model sensor values. For example, in a powerdistribution environment, output parameters of power load or demandfactor might be a function of multiple sensor data values. The operatingparameters of the virtual system model that mimic the operation of thesensor will be adjusted to reflect the real-time data received fromthose sensors. In one embodiment, authorization from a systemadministrator is requested prior to the operating parameters of thevirtual system model being adjusted. This is to ensure that the systemadministrator is aware of the changes that are being made to the virtualsystem model. In one embodiment, after the completion of all the variouscalibration operations, a report is generated to provide a summary ofall the adjustments that have been made to the virtual system model.

As described above, virtual system modeling engine 124 can be configuredto model various aspects of the system to produce predicted values forthe operation of various components within monitored system 102. Thesepredicted values can be compared to actual values being received viadata acquisition hub 112. If the differences are greater than a certainthreshold, e.g., the DTT, but not in an alarm condition, then acalibration instruction can be generated. The calibration instructioncan cause a calibration engine 134 to update the virtual model beingused by system modeling engine 124 to reflect the new operatinginformation.

It will be understood that as monitored system 102 ages, or morespecifically the components comprising monitored system 102 age, thenthe operating parameters, e.g., currents and voltages associated withthose components will also change. Thus, the process of calibrating thevirtual model based on the actual operating information provides amechanism by which the virtual model can be aged along with themonitored system 102 so that the comparisons being generated byanalytics engine 118 are more meaningful.

At a high level, this process can be illustrated with the aid of FIG. 9,which is a flow chart illustrating an example method for updating thevirtual model in accordance with one embodiment. In step 902, data iscollected from, e.g., sensors 104,106, and 108. For example, the sensorscan be configured to monitor protective devices within an electricaldistribution system to determine and monitor the ability of theprotective devices to withstand faults, which is describe in more detailbelow.

In step 904, the data from the various sensors can be processed byanalytics engine 118 in order to evaluate various parameters related tomonitored system 102. In step 905, simulation engine 124 can beconfigured to generate predicted values for monitored system 102 using avirtual model of the system that can be compared to the parametersgenerated by analytics engine 118 in step 904. If there are differencesbetween the actual values and the predicted values, then the virtualmodel can be updated to ensure that the virtual model ages with theactual system 102.

It should be noted that as the monitored system 102 ages, variouscomponents can be repaired, replaced, or upgraded, which can also createdifferences between the simulated and actual data that is not an alarmcondition. Such activity can also lead to calibrations of the virtualmodel to ensure that the virtual model produces relevant predictedvalues. Thus, not only can the virtual model be updated to reflect agingof monitored system 102, but it can also be updated to reflectretrofits, repairs, etc.

As noted above, in certain embodiments, a logical model of a facilitieselectrical system, a data acquisition system (data acquisition hub 112),and power system simulation engines (modeling engine 124) can beintegrated with a logic and methods based approach to the adjustment ofkey database parameters within a virtual model of the electrical systemto evaluate the ability of protective devices within the electricaldistribution system to withstand faults and also effectively “age” thevirtual system with the actual system.

Only through such a process can predictions on the withstand abilitiesof protective devices, and the status, security and health of anelectrical system be accurately calculated. Accuracy is important as thepredictions can be used to arrive at actionable, mission critical orbusiness critical conclusions that may lead to the re-alignment of theelectrical distribution system for optimized performance or security.

FIGS. 10-12 are flow charts presenting logical flows for determining theability of protective devices within an electrical distribution systemto withstand faults and also effectively “age” the virtual system withthe actual system in accordance with one embodiment. FIG. 10 is adiagram illustrating an example process for monitoring the status ofprotective devices in a monitored system 102 and updating a virtualmodel based on monitored data. First, in step 1002, the status of theprotective devices can be monitored in real time. As mentioned,protective devices can include fuses, switches, relays, and circuitbreakers. Accordingly, the status of the fuses/switches, relays, and orcircuit breakers, e.g., the open/close status, source and load status,and on or off status, can be monitored in step 1002. It can bedetermined, in step 1004, if there is any change in the status of themonitored devices. If there is a change, then in step 1006, the virtualmodel can be updated to reflect the status change, i.e., thecorresponding virtual components data can be updated to reflect theactual status of the various protective devices.

In step 1008, predicted values for the various components of monitoredsystem 102 can be generated. But it should be noted that these valuesare based on the current, real-time status of the monitored system. Realtime sensor data can be received in step 1012. This real time data canbe used to monitor the status in step 1002 and it can also be comparedwith the predicted values in step 1014. As noted above, the differencebetween the predicted values and the real time data can also bedetermined in step 1014.

Accordingly, meaningful predicted values based on the actual conditionof monitored system 102 can be generated in steps 1004 to 1010. Thesepredicted values can then be used to determine if further action shouldbe taken based on the comparison of step 1014. For example, if it isdetermined in step 1016 that the difference between the predicted valuesand the real time sensor data is less than or equal to a certainthreshold, e.g., DTT, then no action can be taken e.g., an instructionnot to perform calibration can be issued in step 1018. Alternatively, ifit is determined in step 1020 that the real time data is actuallyindicative of an alarm situation, e.g., is above an alarm threshold,then a do not calibrate instruction can be generated in step 1018 and analarm can be generated as described above. If the real time sensor datais not indicative of an alarm condition, and the difference between thereal time sensor data and the predicted values is greater than thethreshold, as determined in step 1022, then an initiate calibrationcommand can be generated in step 1024.

If an initiate calibration command is issued in step 1024, then afunction call to calibration engine 134 can be generated in step 1026.The function call will cause calibration engine 134 to update thevirtual model in step 1028 based on the real time sensor data. Acomparison between the real time data and predicted data can then begenerated in step 1030 and the differences between the two computed. Instep 1032, a user can be prompted as to whether or not the virtual modelshould in fact be updated. In other embodiments, the update can beautomatic, and step 1032 can be skipped. In step 1034, the virtual modelcould be updated. For example, the virtual model loads, buses, demandfactor, and/or percent running information can be updated based on theinformation obtained in step 1030. An initiate simulation instructioncan then be generated in step 1036, which can cause new predicted valuesto be generated based on the update of virtual model.

In this manner, the predicted values generated in step 1008 are not onlyupdated to reflect the actual operational status of monitored system102, but they are also updated to reflect natural changes in monitoredsystem 102 such as aging. Accordingly, realistic predicted values can begenerated in step 1008.

FIG. 11 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored instep 1002. Depending on the embodiment, the protective devices can beevaluated in terms of the International Electrotechnical Commission(IEC) standards or in accordance with the United States or AmericanNational Standards Institute (ANSI) standards. It will be understood,that the process described in relation to FIG. 11 is not dependent on aparticular standard being used.

First, in step 1102, a short circuit analysis can be performed for theprotective device. Again, the protective device can be any one of avariety of protective device types. For example, the protective devicecan be a fuse or a switch, or some type of circuit breaker. It will beunderstood that there are various types of circuit breakers includingLow Voltage Circuit Breakers (LVCBs). High Voltage Circuit Breakers(HVCBs), Mid Voltage Circuit Breakers (MVCBs), Miniature CircuitBreakers (MCBs), Molded Case Circuit Breakers (MCCBs), Vacuum CircuitBreakers, and Air Circuit Breakers, to name just a few. Any one of thesevarious types of protective devices can be monitored and evaluated usingthe processes illustrated with respect to FIGS. 10-12.

For example, for LVCBs, or MCCBs, the short circuit current, symmetric(I_(sym)) or asymmetric (I_(sym)), and/or the peak current (I_(peak))can be determined in step 1102. For, e.g., LVCBs that are notinstantaneous trip circuit breakers, the short circuit current at adelayed time (I_(symdelay)) can be determined. For HVCBs, a first cycleshort circuit current (I_(sym)) and/or I_(peak) can be determined instep 1102. For fuses or switches, the short circuit current, symmetricor asymmetric, can be determined in step 1102. And for MVCBs the shortcircuit current interrupting time can be calculated. These are just someexamples of the types of short circuit analysis that can be performed inStep 1102 depending on the type of protective device being analyzed.

Once the short circuit analysis is performed in step 1102, various stepscan be carried out in order to determine the bracing capability of theprotective device. For example, if the protective device is a fuse orswitch, then the steps on the left hand side of FIG. 11 can be carriedout. In this case, the fuse rating can first be determined in step 1104.In this case, the fuse rating can be the current rating for the fuse.For certain fuses, the X/R can be calculated in step 1105 and theasymmetric short circuit current (I_(asym)) for the fuse can bedetermined in step 1106 using equation 1.

I _(ASYM) =I _(SYM)√{square root over (1+2e ^(−2p/(X/R)))}  Eq 1

In other implementations, the inductants/reactants (X/R) ratio can becalculated in step 1108 and compared to a fuse test X/R to determine ifthe calculated X/R is greater than the fuse test X/R. The calculated X/Rcan be determined using the predicted values provided in step 1008.Various standard tests X/R values can be used for the fuse test X/Rvalues in step 1108. For example, standard test X/R values for a LVCBcan be as follows:

PCB,ICCB=6.59

MCCB,ICCB rated <=10,000 A=1.73

MCCB,ICCB rated 10,001-20,000 A=3.18

MCCB,ICCB rated>20,000 A=4.9

If the calculated X/R is greater than the fuse test X/R, then in step1112, equation 12 can be used to calculate an adjusted symmetrical shortcircuit current (I_(adjsym)).

$\begin{matrix}{I_{ADJSYM} = {I_{SYM}\left\{ \frac{\sqrt{1 + {2^{{- 2}{p/{({{CALCX}/R})}}}}}}{\sqrt{1 + {2^{{- 2}{p/{({{TESTX}/R})}}}}}} \right\}}} & {{Eq}\mspace{14mu} 12}\end{matrix}$

If the calculated X/R is not greater than the fuse test X/R then Iadjsymcan be set equal to I_(sym) in step 1110. In step 1114, it can then bedetermined if the fuse rating (step 1104) is greater than or equal toI_(adjsym) or I_(asym). If it is, then it can determine in step 1118that the protected device has passed and the percent rating can becalculated in step 1120 as follows:

${\% \mspace{14mu} {rating}} = \frac{I_{ADJSYM}}{{Device}\mspace{14mu} {rating}}$or${\% \mspace{14mu} {rating}} = \frac{I_{ASYM}}{{Device}\mspace{14mu} {rating}}$

If it is determined in step 1114 that the device rating is not greaterthan or equal to I_(adjsym), then it can be determined that the deviceas failed in step 1116. The percent rating can still be calculating instep 1120.

For LVCBs, it can first be determined whether they are fused in step1122. If it is determined that the LVCB is not fused, then in step 1124can be determined if the LVCB is an instantaneous trip LVCB. If it isdetermined that the LVCB is an instantaneous trip LVCB, then in step1130 the first cycle fault X/R can be calculated and compared to acircuit breaker test X/R (see example values above) to determine if thefault X/R is greater than the circuit breaker test X/R. If the fault X/Ris not greater than the circuit breaker test X/R, then in step 1132 itcan be determined if the LVCB is peak rated. If it is peak rated, thenI_(peak), can be used in step 1146 below. If it is determined that theLVCB is not peak rated in step 1132, then I_(adjsym) can be set equal toI_(sym) in step 1140. In step 1146, it can be determined if the devicerating is greater or equal to I_(adjsym) or to I_(peak) as appropriate,for the LVCB.

If it is determined that the device rating is greater than or equal toI_(adjsym), then it can be determined that the LVCB has passed in step1148. The percent rating can then be determined using the equations forI_(adjsym) defined above (step 1120) in step 1152. If it is determinedthat the device rating is not greater than or equal to I_(adjsym), thenit can be determined that the device has failed in step 1150. Thepercent rating can still be calculated in step 1152.

If the calculated fault X/R is greater than the circuit breaker test X/Ras determined in step 1130, then it can be determined if the LVCB ispeak rated in step 1134. If the LVCB is not peak rated, then theI_(adjsym) can be determined using equation 12. If the LVCB is peakrated, then I_(peak) can be determined using equation 11.

I _(PEAK)=√{square root over (2)}I _(SYM){1.02+0.98e ^(−3/(X/R))}  Eq 11

It can then be determined if the device rating is greater than or equalto I_(adjsym) or I_(peak) as appropriate. The pass/fail determinationscan then be made in steps 1148 and 1150 respectively, and the percentrating can be calculated in step 1152.

${\% \mspace{14mu} {rating}} = \frac{I_{ADJSYM}}{{Device}\mspace{14mu} {rating}}$or${\% \mspace{14mu} {rating}} = \frac{I_{PEAK}}{{Device}\mspace{14mu} {rating}}$

If the LVCB is not an instantaneous trip LVCB as determined in step1124, then a time delay calculation can be performed at step 1128followed by calculation of the fault X/R and a determination of whetherthe fault X/R is greater than the circuit breaker test X/R. If it isnot, then Iadjsym can be set equal to Isym in step 1136. If thecalculated fault at X/R is greater than the circuit breaker test X/R,then Iadjsymdelay can be calculated in step 1138 using the followingequation with, e.g., a 0.5 second maximum delay:

$\begin{matrix}{I_{\underset{DELAY}{ADJSYM}} = {I_{\underset{DELAY}{SYM}}\left\{ \frac{\sqrt{1 + {2^{{- 60}{p/{({{CALCX}/R})}}}}}}{\sqrt{1 + {2^{{- 60}{p/{({{TESTX}/R})}}}}}} \right\}}} & {{Eq}\mspace{14mu} 14}\end{matrix}$

It can then be determined if the device rating is greater than or equalto I_(adjsym) or I_(adjsymdelay). The pass/fail determinations can thenbe made in steps 1148 and 1150, respectively and the percent rating canbe calculated in step 1152.

If it is determined that the LVCB is fused in step 1122, then the faultX/R can be calculated in step 1126 and compared to the circuit breakertest X/R in order to determine if the calculated fault X/R is greaterthan the circuit breaker test X/R. If it is greater, then I_(adjsym) canbe calculated in step 1154 using the following equation:

$\begin{matrix}{I_{ADJSYM} = {I_{SYM}\left\{ \frac{1.02 + {0.98^{{- 3}/{({{CALCX}/R})}}}}{1.02 + {0.98^{{- 3}/{({{TESTX}/R})}}}} \right\}}} & {{Eq}\mspace{14mu} 13}\end{matrix}$

If the calculated fault X/R is not greater than the circuit breaker testX/R, then I_(adjsym) can be set equal to I_(sym) in step 1156. It canthen be determined if the device rating is greater than or equal toI_(adjsym) in step 1146. The pass/fail determinations can then becarried out in steps 1148 and 1150 respectively, and the percent ratingcan be determined in step 1152.

FIG. 12 is a diagram illustrating an example process for determining theprotective capabilities of a HVCB. In certain embodiments, X/R can becalculated in step 1157 and a peak current (I_(peak)) can be determinedusing equation 11 in step 1158. In step 1162, it can be determinedwhether the HVCB's rating is greater than or equal to I_(peak) asdetermined in step 1158. If the device rating is greater than or equalto I_(peak), then the device has passed in step 1164. Otherwise, thedevice fails in step 1166. In either case, the percent rating can bedetermined in step 1168 using the following:

${\% \mspace{14mu} {rating}} = \frac{I_{PEAK}}{{Device}\mspace{14mu} {rating}}$

In other embodiments, an interrupting time calculation can be made instep 1170. In such embodiments, a fault X/R can be calculated and thencan be determined if the fault X/R is greater than or equal to a circuitbreaker test X/R in step 1172. For example, the following circuitbreaker test X/R can be used;

50 Hz Test X/R=13.7

60 Hz Test X/R=16.7

(DC Time Constant=0.45 ms)

If the fault X/R is not greater than the circuit breaker test X/R thenI_(adjintsym) can be set equal to I_(sym) in step 1174. If thecalculated fault X/R is greater than the circuit breaker test X/R, thencontact parting time for the circuit breaker can be determined in step1176 and equation 15 can then be used to determine I_(adjintsym) in step1178.

$\begin{matrix}{I_{\underset{SYM}{ADJINT}} = {I_{\underset{SYM}{INT}}\left\{ \frac{\sqrt{1 + {2^{{- 4_{p}}f_{*{t/{({{CALCX}/R})}}}}}}}{\sqrt{1 + {2^{{- 4_{p}}f_{*{t/{({{TESTX}/R})}}}}}}} \right\}}} & {{Eq}\mspace{14mu} 15}\end{matrix}$

In step 1180, it can be determined whether the device rating is greaterthan or equal to Iadjintsym. The pass/fail determinations can then bemade in steps 1182 and 1184 respectively and the percent rating can becalculated in step 1186 using the following:

${\% \mspace{14mu} {rating}} = \frac{I_{ADJINTSYM}}{{Device}\mspace{14mu} {rating}}$

FIG. 13 is a flowchart illustrating an example process for determiningthe protective capabilities of the protective devices being monitored instep 1002 in accordance with another embodiment. The process can startwith a short circuit analysis in step 1302. For systems operating at afrequency other than 60 hz, the protective device X/R can be modified asfollows:

(X/R)mod=(X/R)*60 H/(system Hz).

For fuses/switches, a selection can be made, as appropriate, between useof the symmetrical rating or asymmetrical rating for the device. TheMultiplying Factor (MF) for the device can then be calculated in step1304. The MF can then be used to determine I_(adjsym) or I_(adjsym). Instep 1306, it can be determined if the device rating is greater than orequal to I_(adjasym) or I_(adjsym). Based on this determination, it canbe determined whether the device passed or failed in steps 1308 and 1310respectively, and the percent rating can be determined in step 1312using the following:

% rating=I _(adjsym)*100/device rating;

or

% rating=I _(adjsym)*100/device rating.

For LVCBs, it can first be determined whether the device is fused instep 1314. If the device is not fused, then in step 1315 it can bedetermined whether the X/R is known for the device. If it is known, thenthe LVF can be calculated for the device in step 1320. It should benoted that the LVF can vary depending on whether the LVCB is aninstantaneous trip device or not. If the X/R is not known, then it canbe determined in step 1317, e.g., using the following:

PCB,ICCB=6.59

MCCB,ICCB rated <=10,000 A=1.73

MCCB,ICCB rated 10,001-20,000 A=3.18

MCCB,ICCB rated>20,000 A=4.9

If the device is fused, then in step 1316 it can again be determinedwhether the X/R is known. If it is known, then the LVF can be calculatedin step 1319. If it is not known, then the X/R can be set equal to,e.g., 4.9.

In step 1321, it can be determined if the LVF is less than 1 and if itis, then the LVF can be set equal to 1. In step 1322I_(intadj) can bedetermined using the following:

MCCB/ICCB/PCB With Instantaneous

Iint,adj=LVF*Isym,rms

PCB Without Instantaneous

Iint,adj=LVFP*Isym,rms(½,Cyc)

int,adj=LVFasym*Isym,rms(3-8 Cyc)

In step 1323, it can be determined whether the device's symmetricalrating is greater than or equal to I_(intadj), and it can be determinedbased on this evaluation whether the device passed or failed in steps1324 and 1325 respectively. The percent rating can then be determined instep 1326 using the following:

% rating=I _(intadj)*100/device rating.

FIG. 14 is a diagram illustrating a process for evaluating the withstandcapabilities of a MVCB in accordance with one embodiment. In step 1328,a determination can be made as to whether the following calculationswill be based on all remote inputs, all local inputs or on a No AC Decay(NACD) ratio. For certain implementations, a calculation can then bemade of the total remote contribution, total local contribution, totalcontribution (I_(intrmssym)), and NACD. If the calculated NACD is equalto zero, then it can be determined that all contributions are local. IfNACD is equal to 1, then it can be determined that all contributions areremote.

If all the contributions are remote, then in step 1332 the remote MF(MFr) can be calculated and I_(int) can be calculated using thefollowing:

I _(int) =MFr*I _(intrmssym)

If all the inputs are local, then MFl can be calculated and I_(int) canbe calculated using the following:

I _(int) =MFl*I _(intrmssym)

If the contributions are from NACD, then the NACD, MFr, MFl, and AMFlcan be calculated. If AMFl is less than 1, then AMFl can be set equalto 1. I_(int) can then be calculated using the following:

I _(int) =AMFl*I _(intrmssym) /S

In step 1338, the 3-phase device duty cycle can be calculated and thenit can be determined in step 1340, whether the device rating is greaterthan or equal to I_(int). Whether the device passed or failed can thenbe determined in steps 1342 and 1344, respectively. The percent ratingcan be determined in step 1346 using the following:

% rating=I _(int)*100/3p device rating.

In other embodiments, it can be determined, in step 1348, whether theuser has selected a fixed MF. If so, then in certain embodiments thepeak duty (crest) can be determined in step 1349 and MFp can be setequal to 2.7 in step 1354. If a fixed MF has not been selected, then thepeak duty (crest) can be calculated in step 1350 and MFp can becalculated in step 1358. In step 1362, the MFp can be used to calculatethe following:

I _(mompeak) =MFp*I _(symrms)

In step 1366, it can be determined if the device peak rating (crest) isgreater than or equal to I_(mompeak). It can then be determined whetherthe device passed or failed in steps 1368 and 1370 respectively, and thepercent rating can be calculated as follows:

% rating=I _(mompeak)*100/device peak(crest)rating.

In other embodiments, if a fixed MF is selected, then a momentary dutycycle (C&L) can be determined in step 1351 and MFm can be set equal to,e.g., 1.6. If a fixed MF has not been selected, then in step 1352 MFmcan be calculated. MFm can then be used to determine the following:

I _(mompeak) =MFm*I _(sysmrms)

It can then be determined in step 1374 whether the device C&L, rmsrating is greater than or equal to I_(momsym). Whether the device passedor failed can then be determined in steps 1376 and 1378 respectively,and the percent rating can be calculated as follows:

% rating=I _(momasym)*100/device C&L,rms rating.

Thus, the above methods provide a mean to determine the withstandcapability of various protective devices, under various conditions andusing various standards, using an aged, up to date virtual model of thesystem being monitored.

The influx of massive sensory data, e.g., provided via sensors 104, 106,and 108, intelligent filtration of this dense stream of data intomanageable and easily understandable knowledge. For example, asmentioned, it is important to be able to assess the real-time ability ofthe power system to provide sufficient generation to satisfy the systemload requirements and to move the generated energy through the system tothe load points. Conventional systems do not make use of an on-line,real-time system snap shot captured by a real-time data acquisitionplatform to perform real time system availability evaluation.

FIG. 15 is a flow chart illustrating an example process for analyzingthe reliability of an electrical power distribution and transmissionsystem, in accordance with one embodiment. First, in step 1502,reliability data can be calculated and/or determined. The inputs used instep 1502 can comprise power flow data, e.g., network connectivity,loads, generations, cables/transformer impedances, etc., which can beobtained from the predicted values generated in step 1008, reliabilitydata associated with each power system component, lists of contingenciesto be considered, which can vary by implementation including by region,site, etc., customer damage (load interruptions) costs, which can alsovary by implementation, and load duration curve information. Otherinputs can include failure rates, repair rates, and requiredavailability of the system and of the various components.

In step 1504 a list of possible outage conditions and contingencies canbe evaluated including loss of utility power supply, generators, UPS,and/or distribution lines and infrastructure. In step 1506, a power flowanalysis for monitored system 102 under the various contingencies can beperformed. This analysis can include the resulting failure rates, repairrates, cost of interruption or downtime versus the required systemavailability, etc. In step 1510, it can be determined if the system isoperating in a deficient state when confronted with a specificcontingency. If it is, then is step 1512, the impact on the system, loadinterruptions, costs, failure duration, system unavailability, etc. canall be evaluated.

After the evaluation of step 1512, or if it is determined that thesystem is not in a deficient state in step 1510, then it can bedetermined if further contingencies need to be evaluated. If so, thenthe process can revert to step 1506 and further contingencies can beevaluated. If no more contingencies are to be evaluated, then a reportcan be generated in step 1514. The report can include a system summary,total and detailed reliability indices, system availability, etc. Thereport can also identify system bottlenecks are potential problem areas.

The reliability indices can be based on the results of credible systemcontingencies involving both generation and transmission outages. Thereliability indices can include load point reliability indices, branchreliability indices, and system reliability indices. For example,various load/bus reliability indices can be determined such asprobability and frequency of failure, expected load curtailed, expectedenergy not supplied, frequency of voltage violations, reactive powerrequired, and expected customer outage cost. The load point indices canbe evaluated for the major load buses in the system and can be used insystem design for comparing alternate system configurations andmodifications.

Overall system reliability indices can include power interruption index,power supply average MW curtailment, power supply disturbance index,power energy curtailment index, severity index, and system availability.For example, the individual load point indices can be aggregated toproduce a set of system indices. These indices are indicators of theoverall adequacy of the composite system to meet the total system loaddemand and energy requirements and can be extremely useful for thesystem planner and management, allowing more informed decisions to bemade both in planning and in managing the system.

The various analysis and techniques can be broadly classified as beingeither Monte Carlo simulation or Contingency Enumeration. The processcan also use AC, DC and fast linear network power flow solutionstechniques and can support multiple contingency modeling, multiple loadlevels, automatic or user-selected contingency enumeration, use avariety of remedial actions, and provides sophisticated reportgeneration.

The analysis of step 1506 can include adequacy analysis of the powersystem being monitored based on a prescribed set of criteria by whichthe system must be judged as being in the success or failed state. Thesystem is considered to be in the failed state if the service at loadbuses is interrupted or its quality becomes unacceptable, i.e., if thereare capacity deficiency, overloads, and/or under/over voltages

Various load models can be used in the process of FIG. 15 includingmulti-step load duration curve, curtailable and Firm, and CustomerOutage Cost models. Additionally, various remedial actions can beproscribed or even initiated including MW and MVAR generation control,generator bus voltage control, phase shifter adjustment, MW generationrescheduling, and load curtailment (interruptible and firm).

In other embodiments, the effect of other variables, such as the weatherand human error can also be evaluated in conjunction with the process ofFIG. 15 and indices can be associated with these factors. For example,FIG. 16 is a flow chart illustrating an example process for analyzingthe reliability of an electrical power distribution and transmissionsystem that takes weather information into account in accordance withone embodiment. Thus, in step 1602, real-time weather data can bereceived, e.g., via a data feed such as an XML feed from NationalOceanic and Atmosphere Administration (NOAA). In step 1604, this datacan be converted into reliability data that can be used in step 1502.

It should also be noted that National Fire Protection Association (NFPA)and the Occupational Safety and Health Association (OSHA) have mandatedthat facilities comply with proper workplace safety standards andconduct Arc Flash studies in order to determine the incident energy,protection boundaries and PPE levels needed to be worn by technicians.Unfortunately, conventional approaches/systems for performing suchstudies do not provide a reliable means for the real-time prediction ofthe potential energy released (in calories per centimeter squared) foran arc flash event. Moreover, no real-time system exists that canpredict the required personal protective equipment (PPE) required tosafely perform repairs as required by NFPA 70E and IEEE 1584.

When a fault in the system being monitored contains an arc, the heatreleased can damage equipment and cause personal injury. It is thelatter concern that brought about the development of the heat exposureprograms referred to above. The power dissipated in the arc radiates tothe surrounding surfaces. The further away from the arc the surface is,the less the energy is received per unit area.

As noted above, conventional approaches are based on highly specializedstatic simulation models that are rigid and non-reflective of thefacilities operational status at the time a technician may be needed toconduct repairs on electrical equipment. But the PPE level required forthe repair, or the safe protection boundary may change based on theactual operational status of the facility and alignment of the powerdistribution system at the time repairs are needed. Therefore, a staticmodel does not provide the real-time analysis that can be critical foraccurate PPE level determination. This is because static systems cannotadjust to the many daily changes to the electrical system that occur ata facility, e.g., motors and pumps may be on or off, on-site generationstatus may have changed by having diesel generators on-line, utilityelectrical feed may also change, etc., nor can they age with thefacility to accurately predict the required PPE levels.

Accordingly, existing systems rely on exhaustive studies to be performedoff-line by a power system engineer or a design professional/specialist.Often the specialist must manually modify a simulation model so that itis reflective of the proposed facility operating condition and thenconduct a static simulation or a series of static simulations in orderto come up with recommended safe working distances, energy calculationsand PPE levels. But such a process is not timely, accurate norefficient, and as noted above can be quite costly.

Using the systems and methods described herein a logical model of afacility electrical system can be integrated into a real-timeenvironment, with a robust AC Arc Flash simulation engine (systemmodeling engine 124), a data acquisition system (data acquisition hub112), and an automatic feedback system (calibration engine 134) thatcontinuously synchronizes and calibrates the logical model to the actualoperational conditions of the electrical system. The ability to re-alignthe simulation model in real-time so that it mirrors the real facilityoperating conditions, coupled with the ability to calibrate and age themodel as the real facility ages, as described above, provides adesirable approach to predicting PPE levels, and safe working conditionsat the exact time the repairs are intended to be performed. Accordingly,facility management can provide real-time compliance with, e.g., NFPA70E and IEEE 1584 standards and requirements.

FIG. 17 is a diagram illustrating an example process for predicting inreal-time various parameters associated with an alternating current (AC)arc flash incident, in accordance with one embodiment. These parameterscan include for example, the arc flash incident energy, arc flashprotection boundary, and required Personal Protective Equipment (PPE)levels, e.g., in order to comply with NFPA-70E and IEEE-1584. First, instep 1702, updated virtual model data can be obtained for the systembeing model, e.g., the updated data of step 1006, and the operatingmodes for the system can be determined. In step 1704, an AC 3-phaseshort circuit analysis can be performed in order to obtain bolted faultcurrent values for the system. In step 1706, e.g., IEEE 1584 equationscan be applied to the bolted fault values and any corresponding arcingcurrents can be calculated in step 1708.

The ratio of arc current to bolted current can then be used, in step1710, to determine the arcing current in a specific protective device,such as a circuit breaker or fuse. A coordinated time-current curveanalysis can be performed for the protective device in step 1712. Instep 1714, the arcing current in the protective device and the timecurrent analysis can be used to determine an associated fault clearingtime, and in step 1716 a corresponding arc energy can be determinedbased on, e.g., IEEE 1584 equations applied to the fault clearing timeand arcing current.

In step 1718, the 100% arcing current can be calculated and for systemsoperating at less than 1 kV the 85% arcing current can also becalculated. In step 1720, the fault clearing time in the protectivedevice can be determined at the 85% arcing current level. In step 1722,e.g., IEEE 1584 equations can be applied to the fault clearing time(determined in step 1720) and the arcing current to determine the 85%arc energy level, and in step 1724 the 100% arcing current can becompared with the 85% arcing current, with the higher of the two beingselected. IEEE 1584 equations, for example, can then be applied to theselected arcing current in step 1726 and the PPE level and boundarydistance can be determined in step 1728. In step 1730, these values canbe output, e.g., in the form of a display or report.

In other embodiments, using the same or a similar procedure asillustrated in FIG. 17, the following evaluations can be made inreal-time and based on an accurate, e.g., aged, model of the system:

-   -   Arc Flash Exposure based on IEEE 1584;    -   Arc Flash Exposure based on NFPA 70E;    -   Network-Based Arc Flash Exposure on AC Systems/Single Branch        Case;    -   Network-Based Arc Flash Exposure on AC Systems/Multiple Branch        Cases;    -   Network Arc Flash Exposure on DC Networks;    -   Exposure Simulation at Switchgear Box, MCC Box, Open Area and        Cable Grounded and Ungrounded;    -   Calculate and Select Controlling Branch(s) for Simulation of Arc        Flash;    -   Test Selected Clothing;    -   Calculate Clothing Required;    -   Calculate Safe Zone with Regard to User Defined Clothing        Category;    -   Simulated Art Heat Exposure at User Selected locations;    -   User Defined Fault Cycle for 3-Phase and Controlling Branches;    -   User Defined Distance for Subject;    -   100% and 85% Arcing Current;    -   100% and 85% Protective Device Time;    -   Protective Device Setting Impact on Arc Exposure Energy;    -   User Defined Label Sizes;    -   Attach Labels to One-Line Diagram for User Review;    -   Plot Energy for Each Bus;    -   Write Results into Excel;    -   View and Print Graphic Label for User Selected Bus(s); and    -   Work permit.

With the insight gained through the above methods, appropriateprotective measures, clothing and procedures can be mobilized tominimize the potential for injury should an arc flash incident occur.Facility owners and operators can efficiently implement a real-timesafety management system that is in compliance with NFPA 70E and IEEE1584 guidelines.

FIG. 18 is a flow chart illustrating an example process for real-timeanalysis of the operational stability of an electrical powerdistribution and transmission system, in accordance with one embodiment.The ability to predict, in real-time, the capability of a power systemto maintain stability and/or recover from various contingency events anddisturbances without violating system operational constraints isimportant. This analysis determines the real-time ability of the powersystem to: 1. sustain power demand and maintain sufficient active andreactive power reserve to cope with ongoing changes in demand and systemdisturbances due to contingencies, 2. operate safely with minimumoperating cost while maintaining an adequate level of reliability, and3. provide an acceptably high level of power quality (maintainingvoltage and frequency within tolerable limits) when operating undercontingency conditions.

In step 1802, the dynamic time domain model data can be updated tore-align the virtual system model in real-time so that it mirrors thereal operating conditions of the facility. The updates to the domainmodel data coupled with the ability to calibrate and age the virtualsystem model of the facility as it ages (i.e., real-time condition ofthe facility), as described above, provides a desirable approach topredicting the operational stability of the electrical power systemoperating under contingency situations. That is, these updates accountfor the natural aging effects of hardware that comprise the totalelectrical power system by continuously synchronizing and calibratingboth the control logic used in the simulation and the actual operatingconditions of the electrical system.

The domain model data includes data that is reflective of both thestatic and non-static (rotating) components of the system. Staticcomponents are those components that are assumed to display no changesduring the time in which the transient contingency event takes place.Typical time frames for disturbance in these types of elements rangefrom a few cycles of the operating frequency of the system up to a fewseconds. Examples of static components in an electrical system includebut are not limited to transformers, cables, overhead lines, reactors,static capacitors, etc. Non-static (rotating) components encompasssynchronous machines including their associated controls (exciters,governors, etc), induction machines, compensators, motor operated valves(MOV), turbines, static var compensators, fault isolation units (Flu),static automatic bus transfer (SABT) units, etc. These various types ofnon-static components can be simulated using various techniques. Forexample:

-   -   For Synchronous Machines: thermal (round rotor) and hydraulic        (salient pole) units can be both simulated either by using a        simple model or by the most complete two-axis including damper        winding representation.    -   For Induction Machines: a complete two-axis model can be used.        Also it is possible to model them by just providing the testing        curves (current, power factor, and torque as a function of        speed).    -   For Motor Operated Valves (MOVs): Two modes of MOV operation are        of interest, namely, opening and closing operating modes. Each        mode of operation consists of five distinct stages, a) start, b)        full speed, c) unseating, d) travel, and e) stall. The system        supports user-defined model types for each of the stages. That        is, “start” may be modeled as a constant current while “full        speed” may be modeled by constant power. This same flexibility        exists for all five distinct stages of the closing mode.    -   For AVR and Excitation Systems: There are a number of models        ranging from rotating (DC and AC) and analogue to static and        digital controls. Additionally, the system offers a user-defined        modeling capability; which can be used to define a new        excitation model.    -   For Governors and Turbines: The system is designed to address        current and future technologies including but not limited to        hydraulic, diesel, gas, and combined cycles with mechanical        and/or digital governors.    -   For Static Var Compensators (SVCs): The system is designed to        address current and future technologies including a number of        solid-state (thyristor) controlled SVC's or even the saturable        reactor types.    -   For Fault Isolation Units (FIUs): The system is designed to        address current and future technologies of FIUs also known as        Current Limiting Devices, are devices installed between the        power source and loads to limit the magnitude of fault currents        that occur within loads connected to the power distribution        networks.    -   For Static Automatic Bus Transfers (SABT): The system is        designed to address current and future technologies of SABT        (i.e., solid-state three phase, dual position, three-pole        switch, etc.)

In one embodiment, the time domain model data includes “built-in”dynamic model data for exciters, governors, transformers, relays,breakers, motors, and power system stabilizers (PSS) offered by avariety of manufactures. For example, dynamic model data for theelectrical power system may be OEM manufacturer supplied control logicfor electrical equipment such as automatic voltage regulators (AVR),governors, under load tap changing transformers, relays, breakersmotors, etc. In another embodiment, in order to cope with recentadvances in power electronic and digital controllers, the time domainmodel data includes “user-defined” dynamic modeling data that is createdby an authorized system administrator in accordance with user-definedcontrol logic models. The user-defined models interacts with the virtualsystem model of the electrical power system through “InterfaceVariables” 1816 that are created out of the user-defined control logicmodels. For example, to build a user-defined excitation model, thecontrols requires that generator terminal voltage to be measured andcompared with a reference quantity (voltage set point). Based on thespecific control logic of the excitation and AVR, the model would thencompute the predicted generator field voltage and return that value backto the application. The user-defined modeling supports a large number ofpre-defined control blocks (functions) that are used to assemble therequired control systems and put them into action in a real-timeenvironment for assessing the strength and security of the power system.In still another embodiment, the time domain model data includes bothbuilt-in dynamic model data and user-defined model data.

Moving on to step 1804, a contingency event can be chosen out of adiverse list of contingency events to be evaluated. That is, theoperational stability of the electrical power system can be assessedunder a number of different contingency event scenarios including butnot limited to a singular event contingency or multiple eventcontingencies (that are simultaneous or sequenced in time). In oneembodiment, the contingency events assessed are manually chosen by asystem administrator in accordance with user requirements. In anotherembodiment, the contingency events assessed are automatically chosen inaccordance with control logic that is dynamically adaptive to pastobservations of the electrical power system. That is the control logic“learns” which contingency events to simulate based on past observationsof the electrical power system operating under various conditions.

Some examples of contingency events include but are not limited to:

-   -   Application/removal of three-phase fault.    -   Application/removal of phase-to-ground fault    -   Application/removal of phase-phase-ground fault.    -   Application/removal of phase-phase fault.    -   Branch Addition.    -   Branch Tripping    -   Starting Induction Motor.    -   Stopping Induction Motor    -   Shunt Tripping.    -   Shunt Addition (Capacitor and/or Induction)    -   Generator Tripping.    -   SVC Tripping.    -   Impact Loading (Load Changing Mechanical Torque on Induction        Machine. With this option it is actually possible to turn an        induction motor to an induction generator)    -   Loss of Utility Power Supply/Generators/LPS/Distribution        Lines/System Infrastructure    -   Load Shedding

In step 1806, a transient stability analysis of the electrical powersystem operating under the various chosen contingencies can beperformed. This analysis can include identification of system weaknessesand insecure contingency conditions. That is, the analysis can predict(forecast) the system's ability to sustain power demand, maintainsufficient active and reactive power reserve, operate safely withminimum operating cost while maintaining an adequate level ofreliability, and provide an acceptably high level of power quality whilebeing subjected to various contingency events. The results of theanalysis can be stored by an associative memory engine 1818 during step1814 to support incremental learning about the operationalcharacteristics of the system. That is, the results of the predictions,analysis, and real-time data may be fed, as needed, into the associativememory engine 1818 for pattern and sequence recognition in order tolearn about the logical realities of the power system. In certainembodiments, engine 1818 can also act as a pattern recognition engine ora Hierarchical Temporal Memory (HTM) engine. Additionally, concurrentinputs of various electrical, environmental, mechanical, and othersensory data can be used to learn about and determine normality andabnormality of business and plant operations to provide a means ofunderstanding failure modes and give recommendations.

In step 1810, it can be determined if the system is operating in adeficient state when confronted with a specific contingency. If it is,then in step 1812, a report is generated providing a summary of theoperational stability of the system. The summary may include generalpredictions about the total security and stability of the system and/ordetailed predictions about each component that makes up the system.

Alternatively, if it is determined that the system is not in a deficientstate in step 1810, then step 1808 can determine if furthercontingencies needs to be evaluated. If so, then the process can revertto step 1806 and further contingencies can be evaluated.

The results of real-time simulations performed in accordance with FIG.18 can be communicated in step 1812 via a report, such as a print out ordisplay of the status. In addition, the information can be reported viaa graphical user interface (thick or thin client) that illustrated thevarious components of the system in graphical format. In suchembodiments, the report can simply comprise a graphical indication ofthe security or insecurity of a component, subsystem, or system,including the whole facility. The results can also be forwarded toassociative memory engine 1818, where they can be stored and madeavailable for predictions, pattern/sequence recognition and ability toimagine, e.g., via memory agents or other techniques, some of which aredescribe below, in step 1820.

The process of FIG. 18 can be applied to a number of needs including butnot limited to predicting system stability due to: Motor starting andmotor sequencing, an example is the assessment of adequacy of a powersystem in emergency start up of auxiliaries; evaluation of theprotections such as under frequency and under-voltage load sheddingschemes, example of this is allocation of required load shedding for apotential loss of a power generation source; determination of criticalclearing time of circuit breakers to maintain stability; anddetermination of the sequence of protective device operations andinteractions.

FIG. 19 is a diagram illustrating how the HTM Pattern Recognition andMachine Learning Engine works in conjunction with the other elements ofthe analytics system to make predictions about the operational aspectsof a monitored system, in accordance with one embodiment. As depictedherein, the HTM Pattern Recognition and Machine Learning Engine 551 ishoused within an analytics server 116 and communicatively connected viaa network connection 114 with a data acquisition hub 112, a clientterminal 128 and a virtual system model database 526. The virtual systemmodel database 526 is configured to store the virtual system model ofthe monitored system. The virtual system model is constantly updatedwith real-time data from the data acquisition hub 112 to effectivelyaccount for the natural aging effects of the hardware that comprise thetotal monitored system, thus, mirroring the real operating conditions ofthe system. This provides a desirable approach to predicting theoperational aspects of the monitored power system operating undercontingency situations.

The HTM Machine Learning Engine 551 is configured to store and processpatterns observed from real-time data fed from the hub 112 and predicteddata output from a real-time virtual system model of the monitoredsystem. These patterns can later be used by the HTM Engine 551 to makereal-time predictions (forecasts) about the various operational aspectsof the system.

The data acquisition hub 112 is communicatively connected via dataconnections 110 to a plurality of sensors that are embedded throughout amonitored system 102. The data acquisition hub 112 may be a standaloneunit or integrated within the analytics server 116 and can be embodiedas a piece of hardware, software, or some combination thereof. In oneembodiment, the data connections 110 are “hard wired” physical dataconnections (e.g., serial, network, etc.). For example, a serial orparallel cable connection between the sensors and the hub 112. Inanother embodiment, the data connections 110 are wireless dataconnections. For example, a radio frequency (RF), BLUETOOTH™, infraredor equivalent connection between the sensor and the hub 112.

Examples of a monitored system includes machinery, factories, electricalsystems, processing plants, devices, chemical processes, biologicalsystems, data centers, aircraft carriers, and the like. It should beunderstood that the monitored system can be any combination ofcomponents whose operations can be monitored with conventional sensorsand where each component interacts with or is related to at least oneother component within the combination.

Continuing with FIG. 19, the client 128 is typically a conventional“thin-client” or “thick client” computing device that may utilize avariety of network interfaces (e.g., web browser, CITRIX™, WINDOWSTERMINAL SERVICES™, telnet, or other equivalent thin-client terminalapplications, etc.) to access, configure, and modify the sensors (e.g.,configuration files, etc.), analytics engine (e.g., configuration files,analytics logic, etc.), calibration parameters (e.g., configurationfiles, calibration parameters, etc.), virtual system modeling engine(e.g., configuration files, simulation parameters, etc.) and virtualsystem model of the system under management (e.g., virtual system modeloperating parameters and configuration files). Correspondingly, in oneembodiment, the data from the various components of the monitored systemand the real-time predictions (forecasts) about the various operationalaspects of the system can be displayed on a client 128 display panel forviewing by a system administrator or equivalent. In another embodiment,the data may be summarized in a hard copy report 1902.

As discussed above, the HTM Machine Learning Engine 551 is configured towork in conjunction with a real-time updated virtual system model of themonitored system to make predictions (forecasts) about certainoperational aspects of the monitored system when it is subjected to acontingency event. For example, where the monitored system is anelectrical power system, in one embodiment the HTM Machine LearningEngine 551 can be used to make predictions about the operationalreliability of an electrical power system in response to contingencyevents such as a loss of power to the system, loss of distributionlines, damage to system infrastructure, changes in weather conditions,etc. Examples of indicators of operational reliability include but arenot limited to failure rates, repair rates, and required availability ofthe power system and of the various components that make up the system.

In another embodiment, the operational aspects relate to an arc flashdischarge contingency event that occurs during the operation of thepower system. Examples of arc flash related operational aspects includebut are not limited to quantity of energy released by the arc flashevent, required personal protective equipment (PPE) for personneloperating within the confines of the system during the arc flash event,and measurements of the arc flash safety boundary area around componentscomprising the power system. In still another embodiment, theoperational aspect relates to the operational stability of the systemduring a contingency event. That is, the system's ability to sustainpower demand, maintain sufficient active and reactive power reserve,operate safely with minimum operating cost while maintaining an adequatelevel of reliability, and provide an acceptably high level of powerquality while being subjected to a contingency event.

FIG. 20 is an illustration of the various cognitive layers that comprisethe neocortical catalyst process used by the HTM Pattern Recognition andMachine Learning Engine to analyze and make predictions about theoperational aspects of a monitored system, in accordance with oneembodiment. As depicted herein, the neocortical catalyst process isexecuted by a neocortical model 2002 that is encapsulated by a real-timesensory system layer 2004, which is itself encapsulated by anassociative memory model layer 2006. Each layer is essential to theoperation of the neocortical catalyst process but the key component isstill the neocortical model 2002. The neocortical model 2002 representsthe “ideal” state and performance of the monitored system and it iscontinually updated in real-time by the sensor layer 2004. The sensorylayer 2004 is essentially a data acquisition system comprised of aplurality of sensors imbedded within the monitored system and configuredto provide real-time data feedback to the neocortical model 2002. Theassociative memory layer observes the interactions between theneocortical model 2002 and the real-time sensory inputs from the sensorylayer 2004 to learn and understand complex relationships inherent withinthe monitored system. As the neocortical model 2002 matures over time,the neocortical catalyst process becomes increasingly accurate in makingpredictions about the operational aspects of the monitored system. Thiscombination of the neocortical model 2002, sensory layer 2004 andassociative memory model layer 2006 works together to learn, refine,suggest and predict similarly to how the human neocortex operates.

As discussed above, the HTM Pattern Recognition and Machine LearningEngine operates by storing and processing patterns observed fromreal-time power system operational data and mimicking the neocorticalcatalyst process of the human neocortex to make forecasts/predictionsabout the future operational aspects of the power system. Although,HTM-based forecasting is a highly accurate “memory-based” method forprocessing historical system output data to make predictions aboutfuture system operational output, the power analytics server can alsoutilized other equally accurate methods for inferring (i.e., predicting)future state system outputs from past system observations. For example,the power analytics server can be configured to employ an adaptiveneural network predictive engine that utilizes a statistics-based methodto produce (i.e., make forecasts) predictive system output(s), which ithas never seen before, by learning (through statistical analyses) how to“map” between the historical inputs and outputs (i.e., a training set ofdata).

FIG. 21 is a logical representation of how a three-layer feed-forwardneural network functions, in accordance with one embodiment. In general,neural network systems can be “trained” to produce predicted/forecastedoutput(s) (which have never been seen before) using historical (known)inputs and outputs. That is, a neural network can be taught (i.e.,learn) how to map between the known inputs and outputs (i.e. trainingset) and therefore have the ability to process “new” inputs to arrive ata predicted output. There are many different types and forms of neuralnetworks. However, one particularly common and useful type is thethree-layer feed-forward neural network.

FIG. 21 shows the three-layer feed-forward neural network where “Layer0” 2102 can be the input layer, “Layer 1” 2104 can be the hidden layer,and “Layer 2” 2106 can be the output layer. The vector x=[x₁ . . .x_(i)]^(T) can represent the input data sequence, the matrix w₀₁ can bethe weight matrix from the input layer to the hidden layer, the matrixw₁₂ can be the weight matrix from the hidden layer to the output layer,H₁ and O_(k) can be the bias for the hidden and output layers, andoutput_(k) 2108 can by the neural network output value(s). As knowninput and output value(s) are fed into the neural network, the matrixweights (w₀₁ and w₁₂) and bias values (H₁ and O_(k)) for the input,hidden, and output layers can be continually and automatically adjusted(i.e., learning) to allow the neural network to make more accuratepredictions/forecasts about the resulting output value(s) when new inputvalue(s) are fed into it.

FIG. 21 can also be described in a more compact form, as depicted inFIG. 22, where it is assumed that “Layer 2” 2104 has k number ofneurons. Each neuron in “Layer 1” 2104 and “Layer 2” 2106 can consist ofa summing junction (E) and an activation function (f). In oneembodiment, the three-layer neural network can be trained utilizing a“back-propagation” algorithm by continually adjusting the networkweights (w_(ij) and W_(jk)) in order to minimize the sum-squared errorfunction using the following:

${E\left( w_{ij} \right)} = {\frac{1}{2}{\sum\limits_{p}{\sum\limits_{j}\left( {{target}_{i}^{p} - {out}_{j}^{(2)}} \right)^{2}}}}$

This can be carried out by a series of gradient descent weight updatesas follows:

${\Delta \; w_{ki}^{(m)}} = {{- \eta}\frac{\partial{E\left( w_{ij} \right)}}{\partial w_{kl}^{(m)}}}$

It should be noted, that it is only the outputs out_(j) ⁽²⁾ of the finallayer (i.e., “Layer 2” 2106) that appears in the error function.However, the final layer outputs will depend on all the earlier layersof weights, and this learning algorithm can adjust them all. That is,the learning algorithm can automatically adjust the outputs out_(y)^((n)) of the earlier (hidden) layers so that they can form appropriateintermediate (hidden) representations.

For a three-layer network, the final outputs can be written as follows:

${out}_{k}^{(2)} = {{f\left( {\sum\limits_{j}{{out}_{j}^{(1)}w_{jk}^{(2)}}} \right)} = {f\left( {\sum\limits_{j}{{f\left( {\sum\limits_{i}{i\; n_{i}w_{ij}^{(1)}}} \right)}w_{jk}^{(2)}}} \right)}}$

Finally, the weight update equations between the output layer (i.e.,“Layer 2” 2106) and the hidden layer (i.e., “Layer 1” 2104) as well asthe input layer (i.e., “Layer 0” 2102) can be represented as follows:

For the neuron in the output layer:

${\Delta \; {w_{hl}^{(2)}(t)}} = {{\eta {\sum\limits_{p}{{{delta}_{l}^{(2)}(t)}{{out}_{h}^{(1)}(t)}}}} + {\alpha \; \Delta \; {w_{hl}^{(2)}\left( {t - 1} \right)}}}$

For the neuron in the hidden layer:

${\Delta \; {w_{hl}^{(1)}(l)}} = {{\eta {\sum\limits_{p}{\left( {\sum\limits_{k}{{{delta}_{k}^{(2)}(t)}{w_{lk}^{(2)}(t)}}} \right){{out}_{l}^{(1)}(t)}\left( {1 - {{out}_{t}^{(1)}(t)}} \right)i\; {n_{h}(t)}}}} + {\alpha \; \Delta \; {w_{hl}^{(1)}\left( {t - 1} \right)}}}$

As such, the weight w_(hl) ⁽²⁾ between neurons h and l can be changed inproportion to the output of neuron h and the delta of neuron l. Theweight changes at “Layer 1” 2104 can then take on the same form as“Layer 2” 2106, but the error at each neuron is “back-propagated” fromeach of the output neurons k via the weights w_(lk) ⁽²⁾. It should benoted that t stands for sequence and usually eta (η) is decreased asalpha (α) is increased so that the total step size does not get toolarge.

Within the context of the various embodiments of the power analyticsserver described previously, the three-layer feed-forward neural networkcan be applied as an “adaptive” power analytics prediction engine. Forexample, a training set of known input/output data would typically besupplied by sensors that are interfaced to the various components thatcomprise the monitored system. As known input/output data is continuallyfed into the neural network in real-time, the various weighting factorsin the neural network automatically self-adjusts (i.e., learns) to allowthe power analytics prediction engine to make more accuratepredictions/forecasts about the health, reliability, and performance ofthe monitored system.

FIG. 23 is an illustration of a matrices depicting how a three-layerfeed-forward neural network can be trained using known inputs and outputvalues, in accordance with one embodiment. As depicted, each row ofpatterns 2302 represents a discrete training data set containing pairsof one or more input (i.e., Input 1 . . . . Input i) and output values(i.e., Target 1 . . . Target 3). In one embodiment, the neural network2304 can learn by minimizing some measure of the error of the targetoutputs (i.e., the actual measured output values) as compared tonetwork's estimated output values. For example, the measure of error canbe the sum squared error (SSE) percentage between the target andestimated output values. As more “teaching patterns” are fed into thenetwork, the various weights of the internal neural network algorithmcan iteratively self-adjust to minimize the resulting SSE percentagebetween the target and estimated output values.

FIGS. 24 and 25 illustrate an example of how training patterns can beused to train and validate the accuracy of a neural network, inaccordance to one embodiment. As depicted, the training set is comprisedof 110 patterns each containing thirty input values and one target peakoutput value. Each of the input values 2402 within the pattern 2401represents data received from one of the components within an electricalpower system and the target peak output value 2404 represents the actualmeasured “Day-Ahead Daily-Load Peak-Value” for the power system. Theestimated peak output value 2406 is the “Day-Ahead Daily-LoadPeak-Value” that was predicted/forecasted using the neural networkalgorithm and the error 2408 represents the SSE percentage between thetarget 2404 and estimated peak 2406 output values. As discussed above,the internal weighting values of the neural network algorithm iscontinually adjusted as each training pattern 2401 is fed into theneural network to train it. Upon the completion of the processing of thetraining patterns, the neural network can be validated to see if theresulting SSE percentage values it generates exceeds a threshold valuewhen the neural network is subjected to an additional set of validationpatterns.

FIG. 26 is an illustration of a flow chart describing a method forutilizing a neural network algorithm utilized to make real-timepredictions about the health, reliability, and performance of anelectrical system, in accordance with one embodiment.

Method 2600 begins with operation 2602 where the analytics enginereceives real-time data output from one or more sensors that areinterfaced with the electrical system (i.e., monitored system).Typically, the sensors are communicatively connected to a dataacquisition hub via an analog or digital data connection. The dataacquisition hub can be a standalone unit or integrated within theanalytics server and embodied as a piece of hardware, software, or somecombination thereof. In one embodiment, the data connection can be a“hard wired” physical data connection (e.g., serial, network, etc.). Forexample, a serial or parallel cable connection between the sensor andthe hub. In another embodiment, the data connection can be a wirelessdata connection. For example, a radio frequency (RF), BLUETOOTH™,infrared or equivalent connection between the sensor and the hub.

The data acquisition hub can be configured to communicate “real-time”data from the electrical system to an analytics server using a networkconnection. In one embodiment, the network connection can be a“hardwired” physical connection. For example, the data acquisition hubcan be communicatively connected (via Category 5 (CAT5), fiber optic orequivalent cabling) to a data server (not shown) that can becommunicatively connected (via CAT5, fiber optic or equivalent cabling)through the Internet and to the analytics server. The analytics serverbeing also communicatively connected with the Internet (via CAT5, fiberoptic, or equivalent cabling). In another embodiment, the networkconnection can be a wireless network connection (e.g., Wi-Fi, WLAN,etc.). For example, utilizing an 802.11b/g or equivalent transmissionformat. In practice, the network connection utilized is dependent uponthe particular requirements of the electrical system.

In operation 2604, predicted data output for the one or more sensorsinterfaced to the monitored system utilizing can be generated utilizinga virtual system model of the electrical system. That is, the poweranalytics server can include a virtual system modeling engine thatutilizes dynamic control logic stored in the virtual system model togenerate the predicted output data. The predicted data is supposed to berepresentative of data that should actually be generated and output fromthe monitored system.

In operation 2606, the virtual system model of the monitored system iscalibrated if a difference between the real-time data output and thepredicted data output exceeds a threshold. That is, a determination ismade as to whether the difference between the real-time data output andthe predicted data output falls between a set value and an alarmcondition value, where if the difference falls between the set value andthe alarm condition value a virtual system model calibration operationcan be initiated.

In step 2608, the real-time data output is processed by the neuralnetwork algorithm. That is, the portion of the real-time data outputthat represents the input data values for the adaptive neural networkprediction engine can be fed into the neural network algorithm therebygenerating one or more predicted/estimated data output valuescorresponding to the input values.

In step 2610, the neural network algorithm is optimized by minimizing ameasure of error between the real-time data output and an estimated dataoutput predicted by the neural network algorithm. That is, the internalweighting factors of the neural network algorithm automaticallyself-adjusts to minimize the measure of error between the knownmonitored system output values (i.e., target output values) measured inreal-time by sensors dispersed throughout the monitored system and theestimated/predicted output values that the neural network algorithmgenerates based on the same given set of input values. For example, in ascenario where the real-time data sensors measure input value A andtarget output value B; the neural network algorithm receives input valueA and then generates an estimated output value C. Target output value Band estimated output value C can then be compared to determine a measureof error. In one embodiment, the measure of error can be the sum squarederror (SSE) percentage between the target and estimated output values.It should be appreciated, however, that SSE is but one statisticalmeasure of error between target and estimated output values and thatessentially any statistical measure of error can be utilized by theneural network algorithm as long as the measurement is reproducible.

In operation 2612, an aspect of the monitored system is forecast usingthe neural network algorithm. For example, the neural network algorithmcan forecast aspects relating to:

-   -   Power System Health and Performance    -   Variations or deviations of electrical system performance from        the power system design parameters. That is, the ability of the        electrical system to resist system output variations or        deviations from defined tolerance limits of the electrical        system    -   Incorporation of performance and behavioral specifications for        all the equipment and components that comprise the electrical        system into a real-time management environment    -   System Reliability and Availability    -   As a function of different system, process and load point        reliability indices    -   Implementation of different technological solutions to achieve        reliability centered maintenance targets and goals    -   Power System Capacity levels    -   As-designed total power capacity of the power system.    -   How much of the total power capacity remains or is available        (ability of the electrical system to maintain availability of        its total power capacity)    -   Present utilized power capacity.    -   Power System Strength and Resilience    -   Dynamic stability predictions across all contingency events    -   Determination of protection system stress and withstand status    -   Determination of system security and stability

The embodiments described herein, can be practiced with other computersystem configurations including hand-held devices, microprocessorsystems, microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers and the like. The embodiments canalso be practiced in distributing computing environments where tasks areperformed by remote processing devices that are linked through anetwork.

It should also be understood that the embodiments described herein canemploy various computer-implemented operations involving data stored incomputer systems. These operations are those requiring physicalmanipulation of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. Further, the manipulations performed are often referred toin terms, such as producing, identifying, determining, or comparing.

Any of the operations that form part of the embodiments described hereinare useful machine operations. The invention also relates to a device oran apparatus for performing these operations. The systems and methodsdescribed herein can be specially constructed for the required purposes,such as the carrier network discussed above, or it may be a generalpurpose computer selectively activated or configured by a computerprogram stored in the computer. In particular, various general purposemachines may be used with computer programs written in accordance withthe teachings herein, or it may be more convenient to construct a morespecialized apparatus to perform the required operations.

The embodiments described herein can also be embodied as computerreadable code on a computer readable medium. The computer readablemedium is any data storage device that can store data, which canthereafter be read by a computer system. Examples of the computerreadable medium include hard drives, network attached storage (NAS),read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetictapes, and other optical and non-optical data storage devices. Thecomputer readable medium can also be distributed over a network coupledcomputer systems so that the computer readable code is stored andexecuted in a distributed fashion.

Although a few embodiments of the present invention have been describedin detail herein, it should be understood, by those of ordinary skill,that the present invention may be embodied in many other specific formswithout departing from the spirit or scope of the invention. Therefore,the present examples and embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details provided therein, but may be modified and practicedwithin the scope of the appended claims.

The invention claimed is:
 1. A system for making real-time forecasts ofa monitored system, comprising: a data acquisition componentcommunicatively connected to a sensor configured to acquire real-timedata output from the monitored system; a power analytics servercommunicatively connected to the data acquisition component, comprising,a virtual system modeling engine configured to generate predicted dataoutput for the monitored system utilizing a virtual system model of themonitored system; an analytics engine configured to monitor thereal-time data output and the predicted data output of the monitoredsystem, and update the virtual system model based on the differencebetween the real-time data output and the predicted data output of themonitored system; and an adaptive prediction engine configured toforecast at least one aspect of the monitored system, the adaptiveprediction engine further configured to minimize a measure of errorbetween the real-time data output and a corresponding forecasted dataoutput predicted; and a client terminal communicatively connected to thepower analytics server, the client terminal configured to display the atleast one forecasted aspect.
 2. The system of claim 1, wherein theadaptive prediction engine is further configured to forecast an aspectof the monitored system using a neutral network algorithm.
 3. The systemof claim 2, wherein the adaptive prediction engine is further configuredto optimize the neural network algorithm.
 4. The system of claim 1,wherein the analytics engine is further configured to synchronize thereal-time data output and the predicted data output of the monitoredsystem.
 5. The system of claim 1, wherein the analytics engine isfurther configured to generate a virtual model calibration requestwhenever the difference between the real-time data output and thepredicted system data falls between a set value and an alarm conditionvalue.
 6. The system of claim 1, wherein the analytics engine is furtherconfigured to update the virtual system model by adjusting operationalparameters of the virtual system model.
 7. The system of claim 1,wherein the analytics engine is further configured to generate areal-time report, wherein the report comprises system summary, total anddetailed system security and stability, identification of systemweaknesses and insure contingency conditions.
 8. The system of claim 1,wherein the monitored system is an electrical system.
 9. The system ofclaim 8, wherein the monitored system is a mission critical electricalsystem.
 10. The system of claim 8, wherein the at least one forecastedaspect is related to at least one from the group consisting of: systemhealth and performance, ability of the electrical system to resistsystem output variations or deviations from defined tolerance limits ofthe monitored system; incorporation of performance and behavioralspecifications for all the equipment and components of the electricalsystem into a real-time management environment; system reliability andavailability; reliability indices as a function of different system,process and load points; implementation of different technologicalsolutions to achieve reliability centered maintenance targets and goals;electrical system capacity levels; as-designed total power capacity ofthe electrical system; ability of the electrical system to mainavailability of its total power capacity; present utilized powercapacity; electrical system strength and resilience; dynamic stabilitypredictions across all contingency events; determination of protectionsystem stress and withstand status; and determination of system securityand stability.
 11. A method for making real-time forecasts of amonitored system, comprising: receiving real-time data output from oneor more sensors interfaced to the monitored system; generating predicteddata output for the one or more sensors interfaced to the monitoredsystem utilizing a virtual system model of the monitored system;updating the virtual system model of the monitored system when adifference between the real-time data output and the predicted dataoutput exceeds a threshold; forecasting at least one aspect of themonitored system; and minimizing a measure of error between thereal-time data output and a corresponding forecasted data output. 12.The method of claim 11, further comprises forecasting at least oneaspect of the monitored system using a neutral network algorithm. 13.The method of claim 12, further comprises optimizing the neural networkalgorithm.
 14. The method of claim 11, further comprising synchronizingthe real-time data output and the predicted data output of the monitoredsystem.
 15. The method of claim 11, further comprising generating avirtual model calibration request whenever the difference between thereal-time data output and the predicted system data falls between a setvalue and an alarm condition value.
 16. The method of claim 11, furthercomprising updating the virtual system model by adjusting operationalparameters of the virtual system model.
 17. The method of claim 11,generating and displaying a real-time report of the monitored system,wherein the report comprises system summary, total and detailed systemsecurity and stability, identification of system weaknesses and insurecontingency conditions.
 18. The method of claim 11, wherein themonitored system is an electrical system.
 19. The method of claim 18,wherein the monitored system is a mission critical electrical system.20. The method of claim 18, wherein the at least one forecasted aspectis related to at least one from the group consisting of: system healthand performance, ability of the electrical system to resist systemoutput variations or deviations from defined tolerance limits of themonitored system; incorporation of performance and behavioralspecifications for all the equipment and components of the electricalsystem into a real-time management environment; system reliability andavailability; reliability indices as a function of different system,process and load points; implementation of different technologicalsolutions to achieve reliability centered maintenance targets and goals;electrical system capacity levels; as-designed total power capacity ofthe electrical system; ability of the electrical system to mainavailability of its total power capacity; present utilized powercapacity; electrical system strength and resilience; dynamic stabilitypredictions across all contingency events; determination of protectionsystem stress and withstand status; and determination of system securityand stability.